{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\user\Dropbox\연구 프로젝트\South Korean Cost Sensitivity and Support for Nuclear Weapons\II Submission\Data Replication\replication.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 4 Apr 2024, 02:19:05
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\user\AppData\Local\Temp\STD6364_000000.tmp"
{txt}
{com}. * Import data
. use masterdata, clear
{txt}
{com}. 
. ********************************************************************************
. ** Main Text
. ********************************************************************************
. 
. ********************************************************************************
. ** Table 2: Logit Regression Analysis of the Treatment Effects on Support for Nuclearization
. ********************************************************************************
. 
. eststo clear
{txt}
{com}. 
. eststo: logit supportA econcost humancost combinedcost, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-716.61919}  
Iteration 1:{space 3}log pseudolikelihood = {res:-684.95235}  
Iteration 2:{space 3}log pseudolikelihood = {res:-684.80652}  
Iteration 3:{space 3}log pseudolikelihood = {res:-684.80648}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:3})}{col 70} = {res}{ralign 6:58.81}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-684.80648}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0444}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportA{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}-1.182953{col 26}{space 2} .1850561{col 37}{space 1}   -6.39{col 46}{space 3}0.000{col 54}{space 4}-1.545656{col 67}{space 3}-.8202498
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-.8721295{col 26}{space 2} .1848012{col 37}{space 1}   -4.72{col 46}{space 3}0.000{col 54}{space 4}-1.234333{col 67}{space 3}-.5099257
{txt}combinedcost {c |}{col 14}{res}{space 2}-1.291484{col 26}{space 2} .1848907{col 37}{space 1}   -6.99{col 46}{space 3}0.000{col 54}{space 4}-1.653863{col 67}{space 3}-.9291046
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  1.01523{col 26}{space 2} .1348396{col 37}{space 1}    7.53{col 46}{space 3}0.000{col 54}{space 4} .7509495{col 67}{space 3} 1.279511
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: logit supportA econcost humancost combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Mili Income, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-716.61919}  
Iteration 1:{space 3}log pseudolikelihood = {res:-651.96591}  
Iteration 2:{space 3}log pseudolikelihood = {res:-651.79383}  
Iteration 3:{space 3}log pseudolikelihood = {res:-651.79382}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:12})}{col 70} = {res}{ralign 6:109.83}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-651.79382}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0905}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportA{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}-1.147933{col 26}{space 2} .1954882{col 37}{space 1}   -5.87{col 46}{space 3}0.000{col 54}{space 4}-1.531083{col 67}{space 3}-.7647828
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-.8883142{col 26}{space 2} .1905692{col 37}{space 1}   -4.66{col 46}{space 3}0.000{col 54}{space 4}-1.261823{col 67}{space 3}-.5148055
{txt}combinedcost {c |}{col 14}{res}{space 2}-1.354129{col 26}{space 2} .1950599{col 37}{space 1}   -6.94{col 46}{space 3}0.000{col 54}{space 4} -1.73644{col 67}{space 3}-.9718188
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-.4084048{col 26}{space 2} .2426931{col 37}{space 1}   -1.68{col 46}{space 3}0.092{col 54}{space 4}-.8840745{col 67}{space 3} .0672649
{txt}{space 9}Age {c |}{col 14}{res}{space 2} .0101133{col 26}{space 2} .0050466{col 37}{space 1}    2.00{col 46}{space 3}0.045{col 54}{space 4} .0002221{col 67}{space 3} .0200045
{txt}{space 9}Edu {c |}{col 14}{res}{space 2} .0141993{col 26}{space 2} .1276894{col 37}{space 1}    0.11{col 46}{space 3}0.911{col 54}{space 4}-.2360674{col 67}{space 3}  .264466
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.3209569{col 26}{space 2} .0997527{col 37}{space 1}   -3.22{col 46}{space 3}0.001{col 54}{space 4}-.5164686{col 67}{space 3}-.1254451
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.2372823{col 26}{space 2} .1224659{col 37}{space 1}   -1.94{col 46}{space 3}0.053{col 54}{space 4}-.4773112{col 67}{space 3} .0027465
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.2080215{col 26}{space 2} .1105204{col 37}{space 1}   -1.88{col 46}{space 3}0.060{col 54}{space 4}-.4246374{col 67}{space 3} .0085944
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2}-.0019199{col 26}{space 2}  .115167{col 37}{space 1}   -0.02{col 46}{space 3}0.987{col 54}{space 4} -.227643{col 67}{space 3} .2238032
{txt}{space 8}Mili {c |}{col 14}{res}{space 2} .1370568{col 26}{space 2} .2463542{col 37}{space 1}    0.56{col 46}{space 3}0.578{col 54}{space 4}-.3457886{col 67}{space 3} .6199022
{txt}{space 6}Income {c |}{col 14}{res}{space 2}  .022145{col 26}{space 2} .0286406{col 37}{space 1}    0.77{col 46}{space 3}0.439{col 54}{space 4}-.0339896{col 67}{space 3} .0782795
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  2.39938{col 26}{space 2}  .766189{col 37}{space 1}    3.13{col 46}{space 3}0.002{col 54}{space 4} .8976771{col 67}{space 3} 3.901083
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. 
. esttab using table1.tex, replace booktabs alignment(l) ///
> b(a3) se scalars("N Obs." "ll Log Likelihood" ) ///
> title(My Table) star(+ 0.10 * 0.05 ** 0.01 *** 0.001) nogaps //
{res}{txt}{p 0 4 2}
(file {bf}
table1.tex{rm}
not found)
{p_end}
(output written to {browse  `"table1.tex"'})

{com}. 
. 
. ********************************************************************************
. ** Figure 2: Marginal Effects on Support for Nuclearization
. ********************************************************************************
. 
. eststo clear
{txt}
{com}. eststo: logit supportA econcost humancost combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Mili Income, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-716.61919}  
Iteration 1:{space 3}log pseudolikelihood = {res:-651.96591}  
Iteration 2:{space 3}log pseudolikelihood = {res:-651.79383}  
Iteration 3:{space 3}log pseudolikelihood = {res:-651.79382}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:12})}{col 70} = {res}{ralign 6:109.83}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-651.79382}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0905}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportA{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}-1.147933{col 26}{space 2} .1954882{col 37}{space 1}   -5.87{col 46}{space 3}0.000{col 54}{space 4}-1.531083{col 67}{space 3}-.7647828
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-.8883142{col 26}{space 2} .1905692{col 37}{space 1}   -4.66{col 46}{space 3}0.000{col 54}{space 4}-1.261823{col 67}{space 3}-.5148055
{txt}combinedcost {c |}{col 14}{res}{space 2}-1.354129{col 26}{space 2} .1950599{col 37}{space 1}   -6.94{col 46}{space 3}0.000{col 54}{space 4} -1.73644{col 67}{space 3}-.9718188
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-.4084048{col 26}{space 2} .2426931{col 37}{space 1}   -1.68{col 46}{space 3}0.092{col 54}{space 4}-.8840745{col 67}{space 3} .0672649
{txt}{space 9}Age {c |}{col 14}{res}{space 2} .0101133{col 26}{space 2} .0050466{col 37}{space 1}    2.00{col 46}{space 3}0.045{col 54}{space 4} .0002221{col 67}{space 3} .0200045
{txt}{space 9}Edu {c |}{col 14}{res}{space 2} .0141993{col 26}{space 2} .1276894{col 37}{space 1}    0.11{col 46}{space 3}0.911{col 54}{space 4}-.2360674{col 67}{space 3}  .264466
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.3209569{col 26}{space 2} .0997527{col 37}{space 1}   -3.22{col 46}{space 3}0.001{col 54}{space 4}-.5164686{col 67}{space 3}-.1254451
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.2372823{col 26}{space 2} .1224659{col 37}{space 1}   -1.94{col 46}{space 3}0.053{col 54}{space 4}-.4773112{col 67}{space 3} .0027465
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.2080215{col 26}{space 2} .1105204{col 37}{space 1}   -1.88{col 46}{space 3}0.060{col 54}{space 4}-.4246374{col 67}{space 3} .0085944
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2}-.0019199{col 26}{space 2}  .115167{col 37}{space 1}   -0.02{col 46}{space 3}0.987{col 54}{space 4} -.227643{col 67}{space 3} .2238032
{txt}{space 8}Mili {c |}{col 14}{res}{space 2} .1370568{col 26}{space 2} .2463542{col 37}{space 1}    0.56{col 46}{space 3}0.578{col 54}{space 4}-.3457886{col 67}{space 3} .6199022
{txt}{space 6}Income {c |}{col 14}{res}{space 2}  .022145{col 26}{space 2} .0286406{col 37}{space 1}    0.77{col 46}{space 3}0.439{col 54}{space 4}-.0339896{col 67}{space 3} .0782795
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  2.39938{col 26}{space 2}  .766189{col 37}{space 1}    3.13{col 46}{space 3}0.002{col 54}{space 4} .8976771{col 67}{space 3} 3.901083
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. margins, dydx(econcost humancost combinedcost)
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,040}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(supportA), predict()}{p_end}
{p2col:dy/dx wrt:}{res:econcost humancost combinedcost}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}-.2510281{col 26}{space 2} .0399118{col 37}{space 1}   -6.29{col 46}{space 3}0.000{col 54}{space 4}-.3292539{col 67}{space 3}-.1728023
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-.1942551{col 26}{space 2} .0400192{col 37}{space 1}   -4.85{col 46}{space 3}0.000{col 54}{space 4}-.2726914{col 67}{space 3}-.1158189
{txt}combinedcost {c |}{col 14}{res}{space 2}-.2961188{col 26}{space 2} .0388517{col 37}{space 1}   -7.62{col 46}{space 3}0.000{col 54}{space 4}-.3722667{col 67}{space 3} -.219971
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot,  plotopts(connect(none)) ytitle("Effects on Support for Nuclearization") ///
> ylab(-0.5(.1)0.1, labsize(small) ) yline(0) ///
> xlabel(1 "Economic Costs" 2 "Human Costs" 3 "Economic & Human Costs", labsize(small)) xscale(r(0.5(1)3.5)) ///
> xtitle("Treatment Conditions") graphregion(color(white)) ///
> title("") note("95% Confidence Interval", position(5)) 
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:_deriv}{p_end}
{res}{txt}
{com}. 
. graph export marginal.tif, width(3000) replace
{txt}{p 0 4 2}
file {bf}
marginal.tif{rm}
saved as
TIFF
format
{p_end}

{com}. 
. 
. ********************************************************************************
. ** Table 3: Logit Regression Analysis of South Koreans' Heterogeneous Cost Sensitivities
. ********************************************************************************
. 
. eststo clear
{txt}
{com}. eststo: logit supportA i.econcost##i.Mili humancost combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Income, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-716.61919}  
Iteration 1:{space 3}log pseudolikelihood = {res:-651.07161}  
Iteration 2:{space 3}log pseudolikelihood = {res:-650.86489}  
Iteration 3:{space 3}log pseudolikelihood = {res:-650.86487}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:110.31}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-650.86487}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0918}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportA{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}1.econcost {c |}{col 14}{res}{space 2}-.9889557{col 26}{space 2}  .228149{col 37}{space 1}   -4.33{col 46}{space 3}0.000{col 54}{space 4} -1.43612{col 67}{space 3}-.5417919
{txt}{space 12} {c |}
{space 8}Mili {c |}
Military..)  {c |}{col 14}{res}{space 2} .2515272{col 26}{space 2} .2608589{col 37}{space 1}    0.96{col 46}{space 3}0.335{col 54}{space 4}-.2597469{col 67}{space 3} .7628013
{txt}{space 12} {c |}
{space 4}econcost#{c |}
{space 8}Mili {c |}
{space 10}1 #{c |}
Military..)  {c |}{col 14}{res}{space 2}-.4305986{col 26}{space 2} .3175887{col 37}{space 1}   -1.36{col 46}{space 3}0.175{col 54}{space 4}-1.053061{col 67}{space 3} .1918638
{txt}{space 12} {c |}
{space 3}humancost {c |}{col 14}{res}{space 2}-.8824867{col 26}{space 2}  .191368{col 37}{space 1}   -4.61{col 46}{space 3}0.000{col 54}{space 4}-1.257561{col 67}{space 3}-.5074124
{txt}combinedcost {c |}{col 14}{res}{space 2}-1.352009{col 26}{space 2}  .196423{col 37}{space 1}   -6.88{col 46}{space 3}0.000{col 54}{space 4}-1.736991{col 67}{space 3}-.9670268
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-.4005249{col 26}{space 2} .2425598{col 37}{space 1}   -1.65{col 46}{space 3}0.099{col 54}{space 4}-.8759333{col 67}{space 3} .0748834
{txt}{space 9}Age {c |}{col 14}{res}{space 2} .0103408{col 26}{space 2} .0050528{col 37}{space 1}    2.05{col 46}{space 3}0.041{col 54}{space 4} .0004375{col 67}{space 3} .0202442
{txt}{space 9}Edu {c |}{col 14}{res}{space 2} .0179331{col 26}{space 2} .1279135{col 37}{space 1}    0.14{col 46}{space 3}0.889{col 54}{space 4}-.2327726{col 67}{space 3} .2686389
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.3203712{col 26}{space 2} .0998495{col 37}{space 1}   -3.21{col 46}{space 3}0.001{col 54}{space 4}-.5160727{col 67}{space 3}-.1246697
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.2399661{col 26}{space 2} .1230404{col 37}{space 1}   -1.95{col 46}{space 3}0.051{col 54}{space 4}-.4811208{col 67}{space 3} .0011887
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.2041986{col 26}{space 2} .1107087{col 37}{space 1}   -1.84{col 46}{space 3}0.065{col 54}{space 4}-.4211837{col 67}{space 3} .0127866
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2} -.006845{col 26}{space 2} .1154981{col 37}{space 1}   -0.06{col 46}{space 3}0.953{col 54}{space 4} -.233217{col 67}{space 3} .2195271
{txt}{space 6}Income {c |}{col 14}{res}{space 2} .0234185{col 26}{space 2} .0285255{col 37}{space 1}    0.82{col 46}{space 3}0.412{col 54}{space 4}-.0324903{col 67}{space 3} .0793274
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.327404{col 26}{space 2} .7707253{col 37}{space 1}    3.02{col 46}{space 3}0.003{col 54}{space 4} .8168104{col 67}{space 3} 3.837998
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: logit supportA i.econcost humancost##i.Mili combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Income, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-716.61919}  
Iteration 1:{space 3}log pseudolikelihood = {res:-649.51272}  
Iteration 2:{space 3}log pseudolikelihood = {res: -649.3241}  
Iteration 3:{space 3}log pseudolikelihood = {res:-649.32409}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:118.15}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-649.32409}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0939}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportA{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}1.econcost {c |}{col 14}{res}{space 2}-1.160245{col 26}{space 2} .1947409{col 37}{space 1}   -5.96{col 46}{space 3}0.000{col 54}{space 4} -1.54193{col 67}{space 3}  -.77856
{txt}{space 1}1.humancost {c |}{col 14}{res}{space 2}-1.149587{col 26}{space 2}  .226375{col 37}{space 1}   -5.08{col 46}{space 3}0.000{col 54}{space 4}-1.593273{col 67}{space 3}-.7058998
{txt}{space 12} {c |}
{space 8}Mili {c |}
Military..)  {c |}{col 14}{res}{space 2}-.0479126{col 26}{space 2} .2614983{col 37}{space 1}   -0.18{col 46}{space 3}0.855{col 54}{space 4}-.5604399{col 67}{space 3} .4646147
{txt}{space 12} {c |}
{space 3}humancost#{c |}
{space 8}Mili {c |}
{space 10}1 #{c |}
Military..)  {c |}{col 14}{res}{space 2} .7149721{col 26}{space 2} .3161059{col 37}{space 1}    2.26{col 46}{space 3}0.024{col 54}{space 4} .0954159{col 67}{space 3} 1.334528
{txt}{space 12} {c |}
combinedcost {c |}{col 14}{res}{space 2}-1.362911{col 26}{space 2}  .194268{col 37}{space 1}   -7.02{col 46}{space 3}0.000{col 54}{space 4}-1.743669{col 67}{space 3}-.9821526
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-.4184277{col 26}{space 2} .2444725{col 37}{space 1}   -1.71{col 46}{space 3}0.087{col 54}{space 4} -.897585{col 67}{space 3} .0607296
{txt}{space 9}Age {c |}{col 14}{res}{space 2} .0104355{col 26}{space 2} .0050713{col 37}{space 1}    2.06{col 46}{space 3}0.040{col 54}{space 4}  .000496{col 67}{space 3} .0203749
{txt}{space 9}Edu {c |}{col 14}{res}{space 2} .0130662{col 26}{space 2} .1272325{col 37}{space 1}    0.10{col 46}{space 3}0.918{col 54}{space 4}-.2363048{col 67}{space 3} .2624373
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.3213467{col 26}{space 2} .0997889{col 37}{space 1}   -3.22{col 46}{space 3}0.001{col 54}{space 4}-.5169294{col 67}{space 3}-.1257641
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.2378974{col 26}{space 2} .1223444{col 37}{space 1}   -1.94{col 46}{space 3}0.052{col 54}{space 4} -.477688{col 67}{space 3} .0018932
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.2177087{col 26}{space 2} .1105039{col 37}{space 1}   -1.97{col 46}{space 3}0.049{col 54}{space 4}-.4342925{col 67}{space 3} -.001125
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2} .0051537{col 26}{space 2} .1145958{col 37}{space 1}    0.04{col 46}{space 3}0.964{col 54}{space 4}  -.21945{col 67}{space 3} .2297573
{txt}{space 6}Income {c |}{col 14}{res}{space 2} .0256196{col 26}{space 2} .0286977{col 37}{space 1}    0.89{col 46}{space 3}0.372{col 54}{space 4}-.0306269{col 67}{space 3}  .081866
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.463189{col 26}{space 2} .7687713{col 37}{space 1}    3.20{col 46}{space 3}0.001{col 54}{space 4} .9564245{col 67}{space 3} 3.969953
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. eststo: logit supportA i.econcost##i.Income_binary humancost combinedcost Gender Age Edu Ideo PID  Inter_intl Know_intl Mili, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-716.61919}  
Iteration 1:{space 3}log pseudolikelihood = {res:-650.50731}  
Iteration 2:{space 3}log pseudolikelihood = {res:-650.33489}  
Iteration 3:{space 3}log pseudolikelihood = {res:-650.33488}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:110.51}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-650.33488}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0925}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportA{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}1.econcost {c |}{col 14}{res}{space 2} -1.31927{col 26}{space 2} .3022276{col 37}{space 1}   -4.37{col 46}{space 3}0.000{col 54}{space 4}-1.911625{col 67}{space 3}-.7269146
{txt}{space 12} {c |}
Income_bin~y {c |}
greater t..  {c |}{col 14}{res}{space 2} .1934291{col 26}{space 2} .1612845{col 37}{space 1}    1.20{col 46}{space 3}0.230{col 54}{space 4}-.1226827{col 67}{space 3}  .509541
{txt}{space 12} {c |}
{space 4}econcost#{c |}
Income_bin~y {c |}
{space 10}1 #{c |}
greater t..  {c |}{col 14}{res}{space 2} .2424482{col 26}{space 2} .3368867{col 37}{space 1}    0.72{col 46}{space 3}0.472{col 54}{space 4}-.4178376{col 67}{space 3} .9027339
{txt}{space 12} {c |}
{space 3}humancost {c |}{col 14}{res}{space 2}-.8884472{col 26}{space 2} .1908607{col 37}{space 1}   -4.65{col 46}{space 3}0.000{col 54}{space 4}-1.262527{col 67}{space 3}-.5143672
{txt}combinedcost {c |}{col 14}{res}{space 2}-1.353851{col 26}{space 2} .1952303{col 37}{space 1}   -6.93{col 46}{space 3}0.000{col 54}{space 4}-1.736496{col 67}{space 3}-.9712071
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-.4184917{col 26}{space 2} .2422837{col 37}{space 1}   -1.73{col 46}{space 3}0.084{col 54}{space 4} -.893359{col 67}{space 3} .0563756
{txt}{space 9}Age {c |}{col 14}{res}{space 2} .0095631{col 26}{space 2}  .005065{col 37}{space 1}    1.89{col 46}{space 3}0.059{col 54}{space 4}-.0003641{col 67}{space 3} .0194904
{txt}{space 9}Edu {c |}{col 14}{res}{space 2}-.0025609{col 26}{space 2} .1278765{col 37}{space 1}   -0.02{col 46}{space 3}0.984{col 54}{space 4}-.2531943{col 67}{space 3} .2480725
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.3189381{col 26}{space 2} .0999114{col 37}{space 1}   -3.19{col 46}{space 3}0.001{col 54}{space 4}-.5147609{col 67}{space 3}-.1231153
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.2440023{col 26}{space 2} .1233482{col 37}{space 1}   -1.98{col 46}{space 3}0.048{col 54}{space 4}-.4857604{col 67}{space 3}-.0022442
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.2096552{col 26}{space 2} .1108619{col 37}{space 1}   -1.89{col 46}{space 3}0.059{col 54}{space 4}-.4269407{col 67}{space 3} .0076302
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2} .0024326{col 26}{space 2} .1153691{col 37}{space 1}    0.02{col 46}{space 3}0.983{col 54}{space 4}-.2236866{col 67}{space 3} .2285518
{txt}{space 8}Mili {c |}{col 14}{res}{space 2} .1145149{col 26}{space 2}  .246038{col 37}{space 1}    0.47{col 46}{space 3}0.642{col 54}{space 4}-.3677108{col 67}{space 3} .5967405
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.481613{col 26}{space 2} .7706864{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .9710953{col 67}{space 3}  3.99213
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. eststo: logit supportA i.econcost humancost##i.Income_binary combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Mili, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-716.61919}  
Iteration 1:{space 3}log pseudolikelihood = {res: -650.3671}  
Iteration 2:{space 3}log pseudolikelihood = {res:-650.18466}  
Iteration 3:{space 3}log pseudolikelihood = {res:-650.18464}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:112.05}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-650.18464}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0927}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportA{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}1.econcost {c |}{col 14}{res}{space 2}-1.150873{col 26}{space 2} .1957313{col 37}{space 1}   -5.88{col 46}{space 3}0.000{col 54}{space 4}  -1.5345{col 67}{space 3} -.767247
{txt}{space 1}1.humancost {c |}{col 14}{res}{space 2}-1.086877{col 26}{space 2} .2770386{col 37}{space 1}   -3.92{col 46}{space 3}0.000{col 54}{space 4}-1.629862{col 67}{space 3}-.5438909
{txt}{space 12} {c |}
Income_bin~y {c |}
greater t..  {c |}{col 14}{res}{space 2} .1743011{col 26}{space 2} .1682826{col 37}{space 1}    1.04{col 46}{space 3}0.300{col 54}{space 4}-.1555267{col 67}{space 3} .5041289
{txt}{space 12} {c |}
{space 3}humancost#{c |}
Income_bin~y {c |}
{space 10}1 #{c |}
greater t..  {c |}{col 14}{res}{space 2} .2965823{col 26}{space 2} .3112945{col 37}{space 1}    0.95{col 46}{space 3}0.341{col 54}{space 4}-.3135437{col 67}{space 3} .9067083
{txt}{space 12} {c |}
combinedcost {c |}{col 14}{res}{space 2}-1.355735{col 26}{space 2} .1952415{col 37}{space 1}   -6.94{col 46}{space 3}0.000{col 54}{space 4}-1.738402{col 67}{space 3}-.9730691
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-.4175315{col 26}{space 2} .2411472{col 37}{space 1}   -1.73{col 46}{space 3}0.083{col 54}{space 4}-.8901714{col 67}{space 3} .0551084
{txt}{space 9}Age {c |}{col 14}{res}{space 2}  .009041{col 26}{space 2} .0050737{col 37}{space 1}    1.78{col 46}{space 3}0.075{col 54}{space 4}-.0009033{col 67}{space 3} .0189853
{txt}{space 9}Edu {c |}{col 14}{res}{space 2} -.001157{col 26}{space 2} .1280553{col 37}{space 1}   -0.01{col 46}{space 3}0.993{col 54}{space 4}-.2521408{col 67}{space 3} .2498269
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.3284851{col 26}{space 2} .0999137{col 37}{space 1}   -3.29{col 46}{space 3}0.001{col 54}{space 4}-.5243124{col 67}{space 3}-.1326579
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.2358564{col 26}{space 2} .1231858{col 37}{space 1}   -1.91{col 46}{space 3}0.056{col 54}{space 4}-.4772962{col 67}{space 3} .0055835
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.2122891{col 26}{space 2} .1108461{col 37}{space 1}   -1.92{col 46}{space 3}0.055{col 54}{space 4}-.4295435{col 67}{space 3} .0049654
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2}-.0008528{col 26}{space 2} .1154016{col 37}{space 1}   -0.01{col 46}{space 3}0.994{col 54}{space 4}-.2270359{col 67}{space 3} .2253302
{txt}{space 8}Mili {c |}{col 14}{res}{space 2} .1187006{col 26}{space 2} .2448886{col 37}{space 1}    0.48{col 46}{space 3}0.628{col 54}{space 4}-.3612721{col 67}{space 3} .5986734
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.532161{col 26}{space 2} .7680117{col 37}{space 1}    3.30{col 46}{space 3}0.001{col 54}{space 4} 1.026885{col 67}{space 3} 4.037436
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. eststo: logit supportA i.econcost##c.Ideo humancost combinedcost Gender Age Edu  PID Inter_intl Know_intl Mili Income, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-716.61919}  
Iteration 1:{space 3}log pseudolikelihood = {res:-651.42721}  
Iteration 2:{space 3}log pseudolikelihood = {res:-651.21294}  
Iteration 3:{space 3}log pseudolikelihood = {res:-651.21291}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:109.73}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-651.21291}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0913}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportA{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}1.econcost {c |}{col 14}{res}{space 2}-1.633314{col 26}{space 2}  .497574{col 37}{space 1}   -3.28{col 46}{space 3}0.001{col 54}{space 4}-2.608541{col 67}{space 3} -.658087
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.3735419{col 26}{space 2}  .111295{col 37}{space 1}   -3.36{col 46}{space 3}0.001{col 54}{space 4}-.5916761{col 67}{space 3}-.1554076
{txt}{space 12} {c |}
{space 4}econcost#{c |}
{space 6}c.Ideo {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2190939{col 26}{space 2} .2065055{col 37}{space 1}    1.06{col 46}{space 3}0.289{col 54}{space 4}-.1856495{col 67}{space 3} .6238372
{txt}{space 12} {c |}
{space 3}humancost {c |}{col 14}{res}{space 2}-.8983276{col 26}{space 2} .1921555{col 37}{space 1}   -4.68{col 46}{space 3}0.000{col 54}{space 4}-1.274945{col 67}{space 3}-.5217098
{txt}combinedcost {c |}{col 14}{res}{space 2}-1.367532{col 26}{space 2}  .197026{col 37}{space 1}   -6.94{col 46}{space 3}0.000{col 54}{space 4}-1.753696{col 67}{space 3}-.9813685
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-.3834189{col 26}{space 2} .2450576{col 37}{space 1}   -1.56{col 46}{space 3}0.118{col 54}{space 4} -.863723{col 67}{space 3} .0968852
{txt}{space 9}Age {c |}{col 14}{res}{space 2} .0104707{col 26}{space 2} .0050619{col 37}{space 1}    2.07{col 46}{space 3}0.039{col 54}{space 4} .0005495{col 67}{space 3} .0203919
{txt}{space 9}Edu {c |}{col 14}{res}{space 2} .0152129{col 26}{space 2} .1275156{col 37}{space 1}    0.12{col 46}{space 3}0.905{col 54}{space 4}-.2347132{col 67}{space 3} .2651389
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.2399487{col 26}{space 2} .1230651{col 37}{space 1}   -1.95{col 46}{space 3}0.051{col 54}{space 4}-.4811519{col 67}{space 3} .0012545
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.2014305{col 26}{space 2} .1105554{col 37}{space 1}   -1.82{col 46}{space 3}0.068{col 54}{space 4}-.4181151{col 67}{space 3} .0152541
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2}-.0050124{col 26}{space 2} .1148851{col 37}{space 1}   -0.04{col 46}{space 3}0.965{col 54}{space 4} -.230183{col 67}{space 3} .2201582
{txt}{space 8}Mili {c |}{col 14}{res}{space 2} .1590666{col 26}{space 2} .2483424{col 37}{space 1}    0.64{col 46}{space 3}0.522{col 54}{space 4}-.3276756{col 67}{space 3} .6458088
{txt}{space 6}Income {c |}{col 14}{res}{space 2} .0224785{col 26}{space 2} .0285817{col 37}{space 1}    0.79{col 46}{space 3}0.432{col 54}{space 4}-.0335406{col 67}{space 3} .0784977
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.473236{col 26}{space 2} .7698754{col 37}{space 1}    3.21{col 46}{space 3}0.001{col 54}{space 4} .9643082{col 67}{space 3} 3.982164
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. eststo: logit supportA i.econcost humancost##c.Ideo combinedcost Gender Age Edu PID Inter_intl Know_intl Mili Income, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-716.61919}  
Iteration 1:{space 3}log pseudolikelihood = {res: -649.3406}  
Iteration 2:{space 3}log pseudolikelihood = {res:-649.15372}  
Iteration 3:{space 3}log pseudolikelihood = {res:-649.15371}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:119.93}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-649.15371}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0941}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportA{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}1.econcost {c |}{col 14}{res}{space 2}-1.135694{col 26}{space 2} .1930498{col 37}{space 1}   -5.88{col 46}{space 3}0.000{col 54}{space 4}-1.514064{col 67}{space 3}-.7573232
{txt}{space 1}1.humancost {c |}{col 14}{res}{space 2} .1075312{col 26}{space 2} .4722309{col 37}{space 1}    0.23{col 46}{space 3}0.820{col 54}{space 4}-.8180244{col 67}{space 3} 1.033087
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.1988388{col 26}{space 2} .1138568{col 37}{space 1}   -1.75{col 46}{space 3}0.081{col 54}{space 4} -.421994{col 67}{space 3} .0243164
{txt}{space 12} {c |}
{space 3}humancost#{c |}
{space 6}c.Ideo {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.4659091{col 26}{space 2} .1994586{col 37}{space 1}   -2.34{col 46}{space 3}0.019{col 54}{space 4}-.8568407{col 67}{space 3}-.0749774
{txt}{space 12} {c |}
combinedcost {c |}{col 14}{res}{space 2} -1.32467{col 26}{space 2} .1924767{col 37}{space 1}   -6.88{col 46}{space 3}0.000{col 54}{space 4}-1.701917{col 67}{space 3}-.9474225
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-.3889828{col 26}{space 2} .2472278{col 37}{space 1}   -1.57{col 46}{space 3}0.116{col 54}{space 4}-.8735404{col 67}{space 3} .0955748
{txt}{space 9}Age {c |}{col 14}{res}{space 2} .0104318{col 26}{space 2} .0050819{col 37}{space 1}    2.05{col 46}{space 3}0.040{col 54}{space 4} .0004715{col 67}{space 3} .0203921
{txt}{space 9}Edu {c |}{col 14}{res}{space 2} .0108697{col 26}{space 2} .1287693{col 37}{space 1}    0.08{col 46}{space 3}0.933{col 54}{space 4}-.2415136{col 67}{space 3} .2632529
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.2447437{col 26}{space 2} .1239119{col 37}{space 1}   -1.98{col 46}{space 3}0.048{col 54}{space 4}-.4876064{col 67}{space 3}-.0018809
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.1829561{col 26}{space 2} .1112441{col 37}{space 1}   -1.64{col 46}{space 3}0.100{col 54}{space 4}-.4009905{col 67}{space 3} .0350784
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2} -.018381{col 26}{space 2} .1159871{col 37}{space 1}   -0.16{col 46}{space 3}0.874{col 54}{space 4}-.2457114{col 67}{space 3} .2089495
{txt}{space 8}Mili {c |}{col 14}{res}{space 2} .1559325{col 26}{space 2} .2506894{col 37}{space 1}    0.62{col 46}{space 3}0.534{col 54}{space 4}-.3354097{col 67}{space 3} .6472746
{txt}{space 6}Income {c |}{col 14}{res}{space 2} .0217677{col 26}{space 2} .0287518{col 37}{space 1}    0.76{col 46}{space 3}0.449{col 54}{space 4}-.0345847{col 67}{space 3} .0781202
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.100582{col 26}{space 2} .7827377{col 37}{space 1}    2.68{col 46}{space 3}0.007{col 54}{space 4} .5664438{col 67}{space 3} 3.634719
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. 
. esttab using table1.tex, replace booktabs alignment(l)  ///
> b(a3) se scalars("N Obs." "ll Log Likelihood" ) ///
> title(My Table) star(+ 0.10 * 0.05 ** 0.01 *** 0.001) nogaps  //
{res}{txt}(output written to {browse  `"table1.tex"'})

{com}. 
. 
. ********************************************************************************
. ** Table 3: Marginal Effect Heterogeneous Cost Sensitivities
. ********************************************************************************
. eststo: logit supportA i.econcost humancost##i.Mili combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Income, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-716.61919}  
Iteration 1:{space 3}log pseudolikelihood = {res:-649.51272}  
Iteration 2:{space 3}log pseudolikelihood = {res: -649.3241}  
Iteration 3:{space 3}log pseudolikelihood = {res:-649.32409}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:118.15}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-649.32409}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0939}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportA{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}1.econcost {c |}{col 14}{res}{space 2}-1.160245{col 26}{space 2} .1947409{col 37}{space 1}   -5.96{col 46}{space 3}0.000{col 54}{space 4} -1.54193{col 67}{space 3}  -.77856
{txt}{space 1}1.humancost {c |}{col 14}{res}{space 2}-1.149587{col 26}{space 2}  .226375{col 37}{space 1}   -5.08{col 46}{space 3}0.000{col 54}{space 4}-1.593273{col 67}{space 3}-.7058998
{txt}{space 12} {c |}
{space 8}Mili {c |}
Military..)  {c |}{col 14}{res}{space 2}-.0479126{col 26}{space 2} .2614983{col 37}{space 1}   -0.18{col 46}{space 3}0.855{col 54}{space 4}-.5604399{col 67}{space 3} .4646147
{txt}{space 12} {c |}
{space 3}humancost#{c |}
{space 8}Mili {c |}
{space 10}1 #{c |}
Military..)  {c |}{col 14}{res}{space 2} .7149721{col 26}{space 2} .3161059{col 37}{space 1}    2.26{col 46}{space 3}0.024{col 54}{space 4} .0954159{col 67}{space 3} 1.334528
{txt}{space 12} {c |}
combinedcost {c |}{col 14}{res}{space 2}-1.362911{col 26}{space 2}  .194268{col 37}{space 1}   -7.02{col 46}{space 3}0.000{col 54}{space 4}-1.743669{col 67}{space 3}-.9821526
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-.4184277{col 26}{space 2} .2444725{col 37}{space 1}   -1.71{col 46}{space 3}0.087{col 54}{space 4} -.897585{col 67}{space 3} .0607296
{txt}{space 9}Age {c |}{col 14}{res}{space 2} .0104355{col 26}{space 2} .0050713{col 37}{space 1}    2.06{col 46}{space 3}0.040{col 54}{space 4}  .000496{col 67}{space 3} .0203749
{txt}{space 9}Edu {c |}{col 14}{res}{space 2} .0130662{col 26}{space 2} .1272325{col 37}{space 1}    0.10{col 46}{space 3}0.918{col 54}{space 4}-.2363048{col 67}{space 3} .2624373
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.3213467{col 26}{space 2} .0997889{col 37}{space 1}   -3.22{col 46}{space 3}0.001{col 54}{space 4}-.5169294{col 67}{space 3}-.1257641
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.2378974{col 26}{space 2} .1223444{col 37}{space 1}   -1.94{col 46}{space 3}0.052{col 54}{space 4} -.477688{col 67}{space 3} .0018932
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.2177087{col 26}{space 2} .1105039{col 37}{space 1}   -1.97{col 46}{space 3}0.049{col 54}{space 4}-.4342925{col 67}{space 3} -.001125
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2} .0051537{col 26}{space 2} .1145958{col 37}{space 1}    0.04{col 46}{space 3}0.964{col 54}{space 4}  -.21945{col 67}{space 3} .2297573
{txt}{space 6}Income {c |}{col 14}{res}{space 2} .0256196{col 26}{space 2} .0286977{col 37}{space 1}    0.89{col 46}{space 3}0.372{col 54}{space 4}-.0306269{col 67}{space 3}  .081866
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.463189{col 26}{space 2} .7687713{col 37}{space 1}    3.20{col 46}{space 3}0.001{col 54}{space 4} .9564245{col 67}{space 3} 3.969953
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est7{txt} stored)

{com}. margins, dydx(humancost) at(Mili==(0 1)) 
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,040}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(supportA), predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.humancost}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 4:Mili} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 4:Mili} = {res:{ralign 1:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.humancost {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.humancost  {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2422136{col 26}{space 2} .0429061{col 37}{space 1}   -5.65{col 46}{space 3}0.000{col 54}{space 4} -.326308{col 67}{space 3}-.1581192
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0927627{col 26}{space 2} .0584559{col 37}{space 1}   -1.59{col 46}{space 3}0.113{col 54}{space 4}-.2073341{col 67}{space 3} .0218088
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, xscale(r(-0.5(1)1.5))  plotopts(connect(none)) ytitle("Effects on Support for Nuclearization") ///
> ylab(-0.4(.1)0.1, labsize(small) ) xtitle("Individual Military Experience") yline(0) xlab(,  labsize(small)) title("") graphregion(color(white)) 
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:Mili}{p_end}
{res}{txt}
{com}. 
. 
. graph save military, replace
{res}{txt}file {bf:military.gph} saved

{com}. 
. eststo: logit supportA i.econcost humancost##c.Ideo combinedcost Gender Age Edu PID Inter_intl Know_intl Mili Income, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-716.61919}  
Iteration 1:{space 3}log pseudolikelihood = {res: -649.3406}  
Iteration 2:{space 3}log pseudolikelihood = {res:-649.15372}  
Iteration 3:{space 3}log pseudolikelihood = {res:-649.15371}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:119.93}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-649.15371}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0941}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportA{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}1.econcost {c |}{col 14}{res}{space 2}-1.135694{col 26}{space 2} .1930498{col 37}{space 1}   -5.88{col 46}{space 3}0.000{col 54}{space 4}-1.514064{col 67}{space 3}-.7573232
{txt}{space 1}1.humancost {c |}{col 14}{res}{space 2} .1075312{col 26}{space 2} .4722309{col 37}{space 1}    0.23{col 46}{space 3}0.820{col 54}{space 4}-.8180244{col 67}{space 3} 1.033087
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.1988388{col 26}{space 2} .1138568{col 37}{space 1}   -1.75{col 46}{space 3}0.081{col 54}{space 4} -.421994{col 67}{space 3} .0243164
{txt}{space 12} {c |}
{space 3}humancost#{c |}
{space 6}c.Ideo {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.4659091{col 26}{space 2} .1994586{col 37}{space 1}   -2.34{col 46}{space 3}0.019{col 54}{space 4}-.8568407{col 67}{space 3}-.0749774
{txt}{space 12} {c |}
combinedcost {c |}{col 14}{res}{space 2} -1.32467{col 26}{space 2} .1924767{col 37}{space 1}   -6.88{col 46}{space 3}0.000{col 54}{space 4}-1.701917{col 67}{space 3}-.9474225
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-.3889828{col 26}{space 2} .2472278{col 37}{space 1}   -1.57{col 46}{space 3}0.116{col 54}{space 4}-.8735404{col 67}{space 3} .0955748
{txt}{space 9}Age {c |}{col 14}{res}{space 2} .0104318{col 26}{space 2} .0050819{col 37}{space 1}    2.05{col 46}{space 3}0.040{col 54}{space 4} .0004715{col 67}{space 3} .0203921
{txt}{space 9}Edu {c |}{col 14}{res}{space 2} .0108697{col 26}{space 2} .1287693{col 37}{space 1}    0.08{col 46}{space 3}0.933{col 54}{space 4}-.2415136{col 67}{space 3} .2632529
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.2447437{col 26}{space 2} .1239119{col 37}{space 1}   -1.98{col 46}{space 3}0.048{col 54}{space 4}-.4876064{col 67}{space 3}-.0018809
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.1829561{col 26}{space 2} .1112441{col 37}{space 1}   -1.64{col 46}{space 3}0.100{col 54}{space 4}-.4009905{col 67}{space 3} .0350784
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2} -.018381{col 26}{space 2} .1159871{col 37}{space 1}   -0.16{col 46}{space 3}0.874{col 54}{space 4}-.2457114{col 67}{space 3} .2089495
{txt}{space 8}Mili {c |}{col 14}{res}{space 2} .1559325{col 26}{space 2} .2506894{col 37}{space 1}    0.62{col 46}{space 3}0.534{col 54}{space 4}-.3354097{col 67}{space 3} .6472746
{txt}{space 6}Income {c |}{col 14}{res}{space 2} .0217677{col 26}{space 2} .0287518{col 37}{space 1}    0.76{col 46}{space 3}0.449{col 54}{space 4}-.0345847{col 67}{space 3} .0781202
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.100582{col 26}{space 2} .7827377{col 37}{space 1}    2.68{col 46}{space 3}0.007{col 54}{space 4} .5664438{col 67}{space 3} 3.634719
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est8{txt} stored)

{com}. margins, dydx(humancost) at(Ideo==(1 2 3)) 
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,040}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(supportA), predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.humancost}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 4:Ideo} = {res:{ralign 1:1}}
{lalign 7:2._at: }{space 0}{lalign 4:Ideo} = {res:{ralign 1:2}}
{lalign 7:3._at: }{space 0}{lalign 4:Ideo} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.humancost {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.humancost  {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0755104{col 26}{space 2}  .063443{col 37}{space 1}   -1.19{col 46}{space 3}0.234{col 54}{space 4}-.1998563{col 67}{space 3} .0488355
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1787932{col 26}{space 2} .0396281{col 37}{space 1}   -4.51{col 46}{space 3}0.000{col 54}{space 4}-.2564628{col 67}{space 3}-.1011236
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.2694295{col 26}{space 2} .0471654{col 37}{space 1}   -5.71{col 46}{space 3}0.000{col 54}{space 4} -.361872{col 67}{space 3} -.176987
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, plotopts(connect(none)) ytitle("Effects on Support for Nuclearization") ///
> xlabel(1 "Conservative" 2 "Moderate" 3 "Liberal", labsize(small)) xscale(r(0.5(1)3.5)) ///  
> ylab(-0.4(.1)0.1, labsize(small)) yline(0) yscale(off) ///
> xtitle("Individual Political Ideology") graphregion(color(white) margin(r+5)) title("")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:Ideo}{p_end}
{res}{txt}
{com}. 
. graph save PI, replace
{res}{txt}file {bf:PI.gph} saved

{com}. 
. graph combine military.gph PI.gph, graphregion(fcolor(white) ilcolor(white) lcolor(white)) note("95% Confidence Interval", position(5) size(vsmall))  
{res}{txt}
{com}. 
. graph export hetero.tif, width(3000) replace
{txt}{p 0 4 2}
file {bf}
hetero.tif{rm}
saved as
TIFF
format
{p_end}

{com}. 
. ********************************************************************************
. ** Online Appendix
. ********************************************************************************
. 
. ********************************************************************************
. ** Table 1: Descriptive Statistics I
. ********************************************************************************
. sum support if Group==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}support {c |}{res}        282    5.180851    1.650813          1          7
{txt}
{com}. sum support if Group==2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}support {c |}{res}        251     4.14741    1.571682          1          7
{txt}
{com}. sum support if Group==3

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}support {c |}{res}        252    4.329365    1.674347          1          7
{txt}
{com}. sum support if Group==4

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}support {c |}{res}        255    3.984314    1.597164          1          7
{txt}
{com}. 
. 
. ********************************************************************************
. ** Table 2: Descriptive Statistics II
. ********************************************************************************
. 
. sum support100A if Group==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}support100A {c |}{res}        282    73.40426    44.26272          0        100
{txt}
{com}. sum support100A if Group==2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}support100A {c |}{res}        251    45.81673    49.92425          0        100
{txt}
{com}. sum support100A if Group==3

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}support100A {c |}{res}        252    53.57143    49.97153          0        100
{txt}
{com}. sum support100A if Group==4

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}support100A {c |}{res}        255    43.13725    49.62419          0        100
{txt}
{com}. 
. ********************************************************************************
. ** Table 4: Summary Statistics and Sample Characteristics
. ********************************************************************************
. eststo clear
{txt}
{com}. tabstat Gender Age Edu Ideo PID Inter_intl Know_intl Mili Income ///
> Denuke UStroops_withdrawal Intl_reputation War Provo Nuclear /// 
> Military Alliance Japan Endurance, format(%9.2g) c(stat) stat(n mean sd min max)

{txt}{ralign 12:Variable} {...}
{c |}         N      Mean        SD       Min       Max
{hline 13}{c +}{hline 50}
{ralign 12:Gender} {...}
{c |}{...}
 {res}     1040       .51        .5         0         1
{txt}{ralign 12:Age} {...}
{c |}{...}
 {res}     1040        44        14        20        69
{txt}{ralign 12:Edu} {...}
{c |}{...}
 {res}     1040       3.9       .54         1         5
{txt}{ralign 12:Ideo} {...}
{c |}{...}
 {res}     1040       2.1       .78         1         3
{txt}{ralign 12:PID} {...}
{c |}{...}
 {res}     1040       2.2       .66         1         3
{txt}{ralign 12:Inter_intl} {...}
{c |}{...}
 {res}     1040       2.9       .79         1         5
{txt}{ralign 12:Know_intl} {...}
{c |}{...}
 {res}     1040       3.2       .78         1         5
{txt}{ralign 12:Mili} {...}
{c |}{...}
 {res}     1040       .39       .49         0         1
{txt}{ralign 12:Income} {...}
{c |}{...}
 {res}     1040         5       2.4         1        11
{txt}{ralign 12:Denuke} {...}
{c |}{...}
 {res}     1040         4       .91         1         5
{txt}{ralign 12:UStroops_w~l} {...}
{c |}{...}
 {res}     1040       3.1         1         1         5
{txt}{ralign 12:Intl_reput~n} {...}
{c |}{...}
 {res}     1040       3.1       .95         1         5
{txt}{ralign 12:War} {...}
{c |}{...}
 {res}     1040       3.3       .98         1         5
{txt}{ralign 12:Provo} {...}
{c |}{...}
 {res}     1040       2.7       1.1         1         5
{txt}{ralign 12:Nuclear} {...}
{c |}{...}
 {res}     1040       2.9       1.1         1         5
{txt}{ralign 12:Military} {...}
{c |}{...}
 {res}     1040       1.9       .75         1         5
{txt}{ralign 12:Alliance} {...}
{c |}{...}
 {res}     1040       2.2       .82         1         5
{txt}{ralign 12:Japan} {...}
{c |}{...}
 {res}     1040       1.9         1         1         5
{txt}{ralign 12:Endurance} {...}
{c |}{...}
 {res}     1040       2.3       1.3         1         6
{txt}{hline 13}{c BT}{hline 50}

{com}. 
. 
. ********************************************************************************
. ** Table 5: Logit Regression Analysis of the Possibility of Full-Scale War
. ********************************************************************************
. 
. eststo clear
{txt}
{com}. 
. eststo: logit War_binary econcost humancost combinedcost, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-595.56041}  
Iteration 1:{space 3}log pseudolikelihood = {res:-594.27881}  
Iteration 2:{space 3}log pseudolikelihood = {res:-594.27708}  
Iteration 3:{space 3}log pseudolikelihood = {res:-594.27708}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:3})}{col 70} = {res}{ralign 6:2.55}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.4655}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-594.27708}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0022}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  War_binary{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2} .1332846{col 26}{space 2} .2024967{col 37}{space 1}    0.66{col 46}{space 3}0.510{col 54}{space 4}-.2636018{col 67}{space 3} .5301709
{txt}{space 3}humancost {c |}{col 14}{res}{space 2} .1898469{col 26}{space 2} .2009042{col 37}{space 1}    0.94{col 46}{space 3}0.345{col 54}{space 4}-.2039181{col 67}{space 3} .5836119
{txt}combinedcost {c |}{col 14}{res}{space 2} .3110781{col 26}{space 2} .1976584{col 37}{space 1}    1.57{col 46}{space 3}0.116{col 54}{space 4}-.0763252{col 67}{space 3} .6984815
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -1.20551{col 26}{space 2} .1414643{col 37}{space 1}   -8.52{col 46}{space 3}0.000{col 54}{space 4}-1.482775{col 67}{space 3}-.9282451
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: logit War_binary econcost humancost combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Mili Income, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-595.56041}  
Iteration 1:{space 3}log pseudolikelihood = {res:-552.89679}  
Iteration 2:{space 3}log pseudolikelihood = {res:-551.83706}  
Iteration 3:{space 3}log pseudolikelihood = {res: -551.8351}  
Iteration 4:{space 3}log pseudolikelihood = {res: -551.8351}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:12})}{col 70} = {res}{ralign 6:76.90}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 9:-551.8351}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0734}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  War_binary{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}-.0279635{col 26}{space 2} .2154049{col 37}{space 1}   -0.13{col 46}{space 3}0.897{col 54}{space 4}-.4501494{col 67}{space 3} .3942223
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-.0459616{col 26}{space 2} .2089203{col 37}{space 1}   -0.22{col 46}{space 3}0.826{col 54}{space 4}-.4554378{col 67}{space 3} .3635145
{txt}combinedcost {c |}{col 14}{res}{space 2}  .265999{col 26}{space 2} .2073336{col 37}{space 1}    1.28{col 46}{space 3}0.200{col 54}{space 4}-.1403673{col 67}{space 3} .6723653
{txt}{space 6}Gender {c |}{col 14}{res}{space 2} .3656444{col 26}{space 2} .2547727{col 37}{space 1}    1.44{col 46}{space 3}0.151{col 54}{space 4}-.1337008{col 67}{space 3} .8649896
{txt}{space 9}Age {c |}{col 14}{res}{space 2}-.0445364{col 26}{space 2}   .00636{col 37}{space 1}   -7.00{col 46}{space 3}0.000{col 54}{space 4}-.0570018{col 67}{space 3} -.032071
{txt}{space 9}Edu {c |}{col 14}{res}{space 2}-.1079916{col 26}{space 2}  .167734{col 37}{space 1}   -0.64{col 46}{space 3}0.520{col 54}{space 4}-.4367443{col 67}{space 3}  .220761
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.0575178{col 26}{space 2} .1089883{col 37}{space 1}   -0.53{col 46}{space 3}0.598{col 54}{space 4}-.2711309{col 67}{space 3} .1560954
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.5057953{col 26}{space 2} .1290401{col 37}{space 1}   -3.92{col 46}{space 3}0.000{col 54}{space 4}-.7587093{col 67}{space 3}-.2528814
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.1833361{col 26}{space 2} .1257919{col 37}{space 1}   -1.46{col 46}{space 3}0.145{col 54}{space 4}-.4298836{col 67}{space 3} .0632115
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2}-.0752702{col 26}{space 2} .1311959{col 37}{space 1}   -0.57{col 46}{space 3}0.566{col 54}{space 4}-.3324094{col 67}{space 3}  .181869
{txt}{space 8}Mili {c |}{col 14}{res}{space 2} .1098212{col 26}{space 2} .2636485{col 37}{space 1}    0.42{col 46}{space 3}0.677{col 54}{space 4}-.4069204{col 67}{space 3} .6265628
{txt}{space 6}Income {c |}{col 14}{res}{space 2}-.0625975{col 26}{space 2} .0337828{col 37}{space 1}   -1.85{col 46}{space 3}0.064{col 54}{space 4}-.1288105{col 67}{space 3} .0036155
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.294944{col 26}{space 2} .8841431{col 37}{space 1}    3.73{col 46}{space 3}0.000{col 54}{space 4} 1.562056{col 67}{space 3} 5.027833
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. esttab using table1.tex, replace booktabs alignment(l) ///
> b(a3) se scalars("N Obs." "ll Log Likelihood" ) ///
> title(My Table) star(+ 0.10 * 0.05 ** 0.01 *** 0.001) nogaps //
{res}{txt}(output written to {browse  `"table1.tex"'})

{com}. 
. 
. ********************************************************************************
. ** Table 6: Logit Regression Analysis of ROK's International Reputation
. ********************************************************************************
. 
. eststo clear
{txt}
{com}. 
. eststo: logit Intl_reputation_binary econcost humancost combinedcost, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -651.3398}  
Iteration 1:{space 3}log pseudolikelihood = {res:-644.13655}  
Iteration 2:{space 3}log pseudolikelihood = {res:-644.12144}  
Iteration 3:{space 3}log pseudolikelihood = {res:-644.12144}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:3})}{col 70} = {res}{ralign 6:14.29}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0025}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-644.12144}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0111}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}Intl_reput~y{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2} .4852351{col 26}{space 2} .1878296{col 37}{space 1}    2.58{col 46}{space 3}0.010{col 54}{space 4} .1170958{col 67}{space 3} .8533745
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-.0004325{col 26}{space 2} .1962959{col 37}{space 1}   -0.00{col 46}{space 3}0.998{col 54}{space 4}-.3851654{col 67}{space 3} .3843004
{txt}combinedcost {c |}{col 14}{res}{space 2} .5273466{col 26}{space 2} .1866444{col 37}{space 1}    2.83{col 46}{space 3}0.005{col 54}{space 4} .1615303{col 67}{space 3}  .893163
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.015231{col 26}{space 2} .1348397{col 37}{space 1}   -7.53{col 46}{space 3}0.000{col 54}{space 4}-1.279512{col 67}{space 3}-.7509498
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: logit Intl_reputation_binary econcost humancost combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Mili Income, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -651.3398}  
Iteration 1:{space 3}log pseudolikelihood = {res:-628.74341}  
Iteration 2:{space 3}log pseudolikelihood = {res:-628.57386}  
Iteration 3:{space 3}log pseudolikelihood = {res:-628.57383}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:12})}{col 70} = {res}{ralign 6:43.96}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-628.57383}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0350}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}Intl_reput~y{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}   .37493{col 26}{space 2} .1918452{col 37}{space 1}    1.95{col 46}{space 3}0.051{col 54}{space 4}-.0010797{col 67}{space 3} .7509398
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-.1003176{col 26}{space 2} .2041085{col 37}{space 1}   -0.49{col 46}{space 3}0.623{col 54}{space 4}-.5003629{col 67}{space 3} .2997277
{txt}combinedcost {c |}{col 14}{res}{space 2} .4939923{col 26}{space 2} .1913943{col 37}{space 1}    2.58{col 46}{space 3}0.010{col 54}{space 4} .1188663{col 67}{space 3} .8691183
{txt}{space 6}Gender {c |}{col 14}{res}{space 2} .3439693{col 26}{space 2} .2469628{col 37}{space 1}    1.39{col 46}{space 3}0.164{col 54}{space 4} -.140069{col 67}{space 3} .8280075
{txt}{space 9}Age {c |}{col 14}{res}{space 2} -.020279{col 26}{space 2} .0053527{col 37}{space 1}   -3.79{col 46}{space 3}0.000{col 54}{space 4}  -.03077{col 67}{space 3}-.0097879
{txt}{space 9}Edu {c |}{col 14}{res}{space 2} -.237405{col 26}{space 2}   .13739{col 37}{space 1}   -1.73{col 46}{space 3}0.084{col 54}{space 4}-.5066843{col 67}{space 3} .0318744
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.0322859{col 26}{space 2} .1001009{col 37}{space 1}   -0.32{col 46}{space 3}0.747{col 54}{space 4}-.2284801{col 67}{space 3} .1639082
{txt}{space 9}PID {c |}{col 14}{res}{space 2} .0718408{col 26}{space 2} .1214826{col 37}{space 1}    0.59{col 46}{space 3}0.554{col 54}{space 4}-.1662607{col 67}{space 3} .3099424
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.2908441{col 26}{space 2} .1121888{col 37}{space 1}   -2.59{col 46}{space 3}0.010{col 54}{space 4}-.5107301{col 67}{space 3}-.0709582
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2} .1859745{col 26}{space 2} .1147387{col 37}{space 1}    1.62{col 46}{space 3}0.105{col 54}{space 4}-.0389093{col 67}{space 3} .4108582
{txt}{space 8}Mili {c |}{col 14}{res}{space 2}-.0416569{col 26}{space 2} .2529946{col 37}{space 1}   -0.16{col 46}{space 3}0.869{col 54}{space 4}-.5375173{col 67}{space 3} .4542035
{txt}{space 6}Income {c |}{col 14}{res}{space 2}-.0065831{col 26}{space 2} .0289775{col 37}{space 1}   -0.23{col 46}{space 3}0.820{col 54}{space 4}-.0633779{col 67}{space 3} .0502117
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8690768{col 26}{space 2}   .79555{col 37}{space 1}    1.09{col 46}{space 3}0.275{col 54}{space 4}-.6901725{col 67}{space 3} 2.428326
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. esttab using table1.tex, replace booktabs alignment(l) ///
> b(a3) se scalars("N Obs." "ll Log Likelihood" ) ///
> title(My Table) star(+ 0.10 * 0.05 ** 0.01 *** 0.001) nogaps //
{res}{txt}(output written to {browse  `"table1.tex"'})

{com}. 
. ********************************************************************************
. ** Table 7: Robustness Check I: Logit Regression Analysis with All Control Variables
. ********************************************************************************
. 
. eststo clear
{txt}
{com}. eststo: logit supportA econcost humancost combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Mili Income Denuke UStroops_withdrawal Intl_reputation War Provo Nuclear Military Alliance Japan Endurance, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-716.61919}  
Iteration 1:{space 3}log pseudolikelihood = {res:-491.57317}  
Iteration 2:{space 3}log pseudolikelihood = {res:-488.56243}  
Iteration 3:{space 3}log pseudolikelihood = {res:-488.56094}  
Iteration 4:{space 3}log pseudolikelihood = {res:-488.56094}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:22})}{col 70} = {res}{ralign 6:258.89}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-488.56094}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.3182}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportA{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}-1.429515{col 26}{space 2} .2318489{col 37}{space 1}   -6.17{col 46}{space 3}0.000{col 54}{space 4}-1.883931{col 67}{space 3}-.9750998
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-1.382404{col 26}{space 2} .2297537{col 37}{space 1}   -6.02{col 46}{space 3}0.000{col 54}{space 4}-1.832713{col 67}{space 3}-.9320948
{txt}combinedcost {c |}{col 14}{res}{space 2}-1.572415{col 26}{space 2} .2397812{col 37}{space 1}   -6.56{col 46}{space 3}0.000{col 54}{space 4}-2.042377{col 67}{space 3}-1.102452
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-.2538273{col 26}{space 2} .2834377{col 37}{space 1}   -0.90{col 46}{space 3}0.371{col 54}{space 4}-.8093549{col 67}{space 3} .3017003
{txt}{space 9}Age {c |}{col 14}{res}{space 2}-.0067932{col 26}{space 2} .0067118{col 37}{space 1}   -1.01{col 46}{space 3}0.311{col 54}{space 4}-.0199482{col 67}{space 3} .0063618
{txt}{space 9}Edu {c |}{col 14}{res}{space 2}-.1043694{col 26}{space 2} .1517696{col 37}{space 1}   -0.69{col 46}{space 3}0.492{col 54}{space 4}-.4018325{col 67}{space 3} .1930936
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.2903275{col 26}{space 2} .1302084{col 37}{space 1}   -2.23{col 46}{space 3}0.026{col 54}{space 4}-.5455313{col 67}{space 3}-.0351237
{txt}{space 9}PID {c |}{col 14}{res}{space 2} -.120013{col 26}{space 2} .1473511{col 37}{space 1}   -0.81{col 46}{space 3}0.415{col 54}{space 4}-.4088157{col 67}{space 3} .1687898
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.1084594{col 26}{space 2} .1401094{col 37}{space 1}   -0.77{col 46}{space 3}0.439{col 54}{space 4}-.3830688{col 67}{space 3} .1661501
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2} -.033662{col 26}{space 2} .1505059{col 37}{space 1}   -0.22{col 46}{space 3}0.823{col 54}{space 4}-.3286482{col 67}{space 3} .2613241
{txt}{space 8}Mili {c |}{col 14}{res}{space 2} .2239493{col 26}{space 2}  .288967{col 37}{space 1}    0.77{col 46}{space 3}0.438{col 54}{space 4}-.3424155{col 67}{space 3} .7903141
{txt}{space 6}Income {c |}{col 14}{res}{space 2} .0000631{col 26}{space 2} .0349984{col 37}{space 1}    0.00{col 46}{space 3}0.999{col 54}{space 4}-.0685324{col 67}{space 3} .0686587
{txt}{space 6}Denuke {c |}{col 14}{res}{space 2} .3265955{col 26}{space 2}  .103999{col 37}{space 1}    3.14{col 46}{space 3}0.002{col 54}{space 4} .1227612{col 67}{space 3} .5304299
{txt}UStroops_w~l {c |}{col 14}{res}{space 2}-.2080235{col 26}{space 2} .1097028{col 37}{space 1}   -1.90{col 46}{space 3}0.058{col 54}{space 4} -.423037{col 67}{space 3} .0069901
{txt}Intl_reput~n {c |}{col 14}{res}{space 2} .8521996{col 26}{space 2} .1086836{col 37}{space 1}    7.84{col 46}{space 3}0.000{col 54}{space 4} .6391836{col 67}{space 3} 1.065216
{txt}{space 9}War {c |}{col 14}{res}{space 2}-.2277952{col 26}{space 2} .1160728{col 37}{space 1}   -1.96{col 46}{space 3}0.050{col 54}{space 4}-.4552938{col 67}{space 3}-.0002966
{txt}{space 7}Provo {c |}{col 14}{res}{space 2} .2311125{col 26}{space 2}  .106224{col 37}{space 1}    2.18{col 46}{space 3}0.030{col 54}{space 4} .0229173{col 67}{space 3} .4393077
{txt}{space 5}Nuclear {c |}{col 14}{res}{space 2}-.1257916{col 26}{space 2} .0939846{col 37}{space 1}   -1.34{col 46}{space 3}0.181{col 54}{space 4} -.309998{col 67}{space 3} .0584147
{txt}{space 4}Military {c |}{col 14}{res}{space 2}-.3177041{col 26}{space 2} .1302054{col 37}{space 1}   -2.44{col 46}{space 3}0.015{col 54}{space 4}-.5729019{col 67}{space 3}-.0625062
{txt}{space 4}Alliance {c |}{col 14}{res}{space 2}-.0991353{col 26}{space 2} .1264605{col 37}{space 1}   -0.78{col 46}{space 3}0.433{col 54}{space 4}-.3469934{col 67}{space 3} .1487228
{txt}{space 7}Japan {c |}{col 14}{res}{space 2}-.8963808{col 26}{space 2} .0980651{col 37}{space 1}   -9.14{col 46}{space 3}0.000{col 54}{space 4}-1.088585{col 67}{space 3}-.7041768
{txt}{space 3}Endurance {c |}{col 14}{res}{space 2} .1713122{col 26}{space 2} .0671447{col 37}{space 1}    2.55{col 46}{space 3}0.011{col 54}{space 4}  .039711{col 67}{space 3} .3029135
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.654765{col 26}{space 2} 1.070692{col 37}{space 1}    2.48{col 46}{space 3}0.013{col 54}{space 4} .5562478{col 67}{space 3} 4.753282
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. 
. esttab using table1.tex, replace booktabs alignment(l) ///
> b(a3) se scalars("N Obs." "ll Log Likelihood" ) ///
> title(My Table) star(+ 0.10 * 0.05 ** 0.01 *** 0.001) nogaps //
{res}{txt}(output written to {browse  `"table1.tex"'})

{com}. 
. ********************************************************************************
. ** Table 8: Robustness Check II: Linear Probability Model
. ********************************************************************************
. 
. eststo clear
{txt}
{com}. 
. eststo: reg support100A econcost humancost combinedcost, robust

{txt}Linear regression                               Number of obs     = {res}     1,040
                                                {txt}F(3, 1036)        =  {res}    23.91
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0593
                                                {txt}Root MSE          =    {res}  48.39

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} support100A{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}-27.58752{col 26}{space 2} 4.108308{col 37}{space 1}   -6.72{col 46}{space 3}0.000{col 54}{space 4}-35.64908{col 67}{space 3}-19.52597
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-19.83283{col 26}{space 2} 4.105813{col 37}{space 1}   -4.83{col 46}{space 3}0.000{col 54}{space 4}-27.88948{col 67}{space 3}-11.77617
{txt}combinedcost {c |}{col 14}{res}{space 2}  -30.267{col 26}{space 2} 4.075038{col 37}{space 1}   -7.43{col 46}{space 3}0.000{col 54}{space 4}-38.26327{col 67}{space 3}-22.27073
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 73.40426{col 26}{space 2} 2.636204{col 37}{space 1}   27.84{col 46}{space 3}0.000{col 54}{space 4} 68.23135{col 67}{space 3} 78.57716
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. eststo: reg support100A econcost humancost combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Mili Income, robust

{txt}Linear regression                               Number of obs     = {res}     1,040
                                                {txt}F(12, 1027)       =  {res}    14.19
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1170
                                                {txt}Root MSE          =    {res} 47.087

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} support100A{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}-25.07166{col 26}{space 2} 4.144274{col 37}{space 1}   -6.05{col 46}{space 3}0.000{col 54}{space 4}-33.20387{col 67}{space 3}-16.93945
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-18.92213{col 26}{space 2} 3.985335{col 37}{space 1}   -4.75{col 46}{space 3}0.000{col 54}{space 4}-26.74246{col 67}{space 3} -11.1018
{txt}combinedcost {c |}{col 14}{res}{space 2}-29.71164{col 26}{space 2}  4.06821{col 37}{space 1}   -7.30{col 46}{space 3}0.000{col 54}{space 4} -37.6946{col 67}{space 3}-21.72869
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-9.283243{col 26}{space 2} 5.411113{col 37}{space 1}   -1.72{col 46}{space 3}0.087{col 54}{space 4}-19.90134{col 67}{space 3} 1.334858
{txt}{space 9}Age {c |}{col 14}{res}{space 2} .2260686{col 26}{space 2} .1123679{col 37}{space 1}    2.01{col 46}{space 3}0.044{col 54}{space 4} .0055717{col 67}{space 3} .4465654
{txt}{space 9}Edu {c |}{col 14}{res}{space 2}  .198507{col 26}{space 2}  2.83923{col 37}{space 1}    0.07{col 46}{space 3}0.944{col 54}{space 4}-5.372847{col 67}{space 3} 5.769861
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-6.961889{col 26}{space 2}  2.20341{col 37}{space 1}   -3.16{col 46}{space 3}0.002{col 54}{space 4}-11.28559{col 67}{space 3}-2.638189
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-5.078822{col 26}{space 2} 2.675388{col 37}{space 1}   -1.90{col 46}{space 3}0.058{col 54}{space 4}-10.32867{col 67}{space 3} .1710299
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-4.601295{col 26}{space 2} 2.430917{col 37}{space 1}   -1.89{col 46}{space 3}0.059{col 54}{space 4}-9.371427{col 67}{space 3} .1688361
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2} .0088105{col 26}{space 2} 2.523553{col 37}{space 1}    0.00{col 46}{space 3}0.997{col 54}{space 4}-4.943099{col 67}{space 3}  4.96072
{txt}{space 8}Mili {c |}{col 14}{res}{space 2} 2.710474{col 26}{space 2} 5.407471{col 37}{space 1}    0.50{col 46}{space 3}0.616{col 54}{space 4}-7.900479{col 67}{space 3} 13.32143
{txt}{space 6}Income {c |}{col 14}{res}{space 2} .5002354{col 26}{space 2} .6264787{col 37}{space 1}    0.80{col 46}{space 3}0.425{col 54}{space 4}-.7290891{col 67}{space 3}  1.72956
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 102.1579{col 26}{space 2} 16.67343{col 37}{space 1}    6.13{col 46}{space 3}0.000{col 54}{space 4} 69.44001{col 67}{space 3} 134.8758
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. eststo: reg support100A econcost humancost combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Mili Income Denuke UStroops_withdrawal Intl_reputation War Provo  Nuclear Military  Alliance Japan Endurance, robust

{txt}Linear regression                               Number of obs     = {res}     1,040
                                                {txt}F(22, 1017)       =  {res}    52.91
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3565
                                                {txt}Root MSE          =    {res} 40.395

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} support100A{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}-22.41622{col 26}{space 2} 3.667443{col 37}{space 1}   -6.11{col 46}{space 3}0.000{col 54}{space 4}-29.61284{col 67}{space 3} -15.2196
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}  -20.007{col 26}{space 2} 3.403476{col 37}{space 1}   -5.88{col 46}{space 3}0.000{col 54}{space 4}-26.68564{col 67}{space 3}-13.32837
{txt}combinedcost {c |}{col 14}{res}{space 2}-24.38531{col 26}{space 2} 3.618522{col 37}{space 1}   -6.74{col 46}{space 3}0.000{col 54}{space 4}-31.48593{col 67}{space 3}-17.28469
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-4.095114{col 26}{space 2} 4.671198{col 37}{space 1}   -0.88{col 46}{space 3}0.381{col 54}{space 4} -13.2614{col 67}{space 3} 5.071174
{txt}{space 9}Age {c |}{col 14}{res}{space 2}-.0854937{col 26}{space 2} .1086773{col 37}{space 1}   -0.79{col 46}{space 3}0.432{col 54}{space 4}-.2987511{col 67}{space 3} .1277637
{txt}{space 9}Edu {c |}{col 14}{res}{space 2}-1.951562{col 26}{space 2} 2.359587{col 37}{space 1}   -0.83{col 46}{space 3}0.408{col 54}{space 4}-6.581779{col 67}{space 3} 2.678654
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-4.778376{col 26}{space 2} 1.999609{col 37}{space 1}   -2.39{col 46}{space 3}0.017{col 54}{space 4}-8.702209{col 67}{space 3}-.8545439
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-1.806918{col 26}{space 2} 2.336963{col 37}{space 1}   -0.77{col 46}{space 3}0.440{col 54}{space 4}-6.392739{col 67}{space 3} 2.778903
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-2.930023{col 26}{space 2}  2.11444{col 37}{space 1}   -1.39{col 46}{space 3}0.166{col 54}{space 4}-7.079188{col 67}{space 3} 1.219141
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2} .2315666{col 26}{space 2} 2.226795{col 37}{space 1}    0.10{col 46}{space 3}0.917{col 54}{space 4}-4.138073{col 67}{space 3} 4.601206
{txt}{space 8}Mili {c |}{col 14}{res}{space 2} 3.260492{col 26}{space 2} 4.615841{col 37}{space 1}    0.71{col 46}{space 3}0.480{col 54}{space 4} -5.79717{col 67}{space 3} 12.31815
{txt}{space 6}Income {c |}{col 14}{res}{space 2} .0316921{col 26}{space 2} .5463751{col 37}{space 1}    0.06{col 46}{space 3}0.954{col 54}{space 4}-1.040459{col 67}{space 3} 1.103844
{txt}{space 6}Denuke {c |}{col 14}{res}{space 2}  5.21268{col 26}{space 2} 1.602542{col 37}{space 1}    3.25{col 46}{space 3}0.001{col 54}{space 4} 2.068012{col 67}{space 3} 8.357348
{txt}UStroops_w~l {c |}{col 14}{res}{space 2}-2.979694{col 26}{space 2} 1.608863{col 37}{space 1}   -1.85{col 46}{space 3}0.064{col 54}{space 4}-6.136766{col 67}{space 3} .1773775
{txt}Intl_reput~n {c |}{col 14}{res}{space 2} 13.65885{col 26}{space 2} 1.603277{col 37}{space 1}    8.52{col 46}{space 3}0.000{col 54}{space 4} 10.51274{col 67}{space 3} 16.80496
{txt}{space 9}War {c |}{col 14}{res}{space 2}-2.936252{col 26}{space 2} 1.783208{col 37}{space 1}   -1.65{col 46}{space 3}0.100{col 54}{space 4}-6.435441{col 67}{space 3} .5629366
{txt}{space 7}Provo {c |}{col 14}{res}{space 2} 3.466215{col 26}{space 2} 1.598148{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .3301694{col 67}{space 3}  6.60226
{txt}{space 5}Nuclear {c |}{col 14}{res}{space 2} -1.75294{col 26}{space 2}  1.44413{col 37}{space 1}   -1.21{col 46}{space 3}0.225{col 54}{space 4}-4.586757{col 67}{space 3} 1.080876
{txt}{space 4}Military {c |}{col 14}{res}{space 2}-4.172813{col 26}{space 2} 1.985152{col 37}{space 1}   -2.10{col 46}{space 3}0.036{col 54}{space 4}-8.068276{col 67}{space 3}-.2773501
{txt}{space 4}Alliance {c |}{col 14}{res}{space 2}-1.331388{col 26}{space 2} 1.926163{col 37}{space 1}   -0.69{col 46}{space 3}0.490{col 54}{space 4}-5.111097{col 67}{space 3} 2.448321
{txt}{space 7}Japan {c |}{col 14}{res}{space 2}-14.03982{col 26}{space 2} 1.289828{col 37}{space 1}  -10.89{col 46}{space 3}0.000{col 54}{space 4}-16.57085{col 67}{space 3}-11.50879
{txt}{space 3}Endurance {c |}{col 14}{res}{space 2} 2.672295{col 26}{space 2} .9919196{col 37}{space 1}    2.69{col 46}{space 3}0.007{col 54}{space 4} .7258518{col 67}{space 3} 4.618738
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 87.20489{col 26}{space 2} 16.96168{col 37}{space 1}    5.14{col 46}{space 3}0.000{col 54}{space 4} 53.92099{col 67}{space 3} 120.4888
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. esttab using table1.tex, replace booktabs alignment(l) ///
> b(a3) se scalars("N Obs." "ll Log Likelihood" ) ///
> title(My Table) star(+ 0.10 * 0.05 ** 0.01 *** 0.001) nogaps //
{res}{txt}(output written to {browse  `"table1.tex"'})

{com}. 
. ********************************************************************************
. ** Table 9: Robustness Check III: Logit Regression Analysis (New Binary DV)
. ********************************************************************************
. 
. eststo clear
{txt}
{com}. eststo: logit supportB econcost humancost combinedcost, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-655.80427}  
Iteration 1:{space 3}log pseudolikelihood = {res:-632.13898}  
Iteration 2:{space 3}log pseudolikelihood = {res:-631.78884}  
Iteration 3:{space 3}log pseudolikelihood = {res:-631.78882}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:3})}{col 70} = {res}{ralign 6:42.80}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-631.78882}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0366}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportB{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}-1.029222{col 26}{space 2} .2045043{col 37}{space 1}   -5.03{col 46}{space 3}0.000{col 54}{space 4}-1.430043{col 67}{space 3}-.6284013
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-.9015774{col 26}{space 2}  .205895{col 37}{space 1}   -4.38{col 46}{space 3}0.000{col 54}{space 4}-1.305124{col 67}{space 3}-.4980306
{txt}combinedcost {c |}{col 14}{res}{space 2}-1.282965{col 26}{space 2} .2018084{col 37}{space 1}   -6.36{col 46}{space 3}0.000{col 54}{space 4}-1.678502{col 67}{space 3}-.8874274
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.559218{col 26}{space 2}  .157238{col 37}{space 1}    9.92{col 46}{space 3}0.000{col 54}{space 4} 1.251037{col 67}{space 3} 1.867399
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: logit supportB econcost humancost combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Mili Income, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-655.80427}  
Iteration 1:{space 3}log pseudolikelihood = {res:-594.42658}  
Iteration 2:{space 3}log pseudolikelihood = {res:-592.97228}  
Iteration 3:{space 3}log pseudolikelihood = {res:-592.96967}  
Iteration 4:{space 3}log pseudolikelihood = {res:-592.96967}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:12})}{col 70} = {res}{ralign 6:105.70}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-592.96967}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0958}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportB{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2} -.982726{col 26}{space 2} .2127802{col 37}{space 1}   -4.62{col 46}{space 3}0.000{col 54}{space 4}-1.399767{col 67}{space 3}-.5656844
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-.9514588{col 26}{space 2} .2098525{col 37}{space 1}   -4.53{col 46}{space 3}0.000{col 54}{space 4}-1.362762{col 67}{space 3}-.5401555
{txt}combinedcost {c |}{col 14}{res}{space 2}-1.355805{col 26}{space 2} .2104643{col 37}{space 1}   -6.44{col 46}{space 3}0.000{col 54}{space 4}-1.768307{col 67}{space 3}-.9433025
{txt}{space 6}Gender {c |}{col 14}{res}{space 2} -.293097{col 26}{space 2} .2556736{col 37}{space 1}   -1.15{col 46}{space 3}0.252{col 54}{space 4} -.794208{col 67}{space 3}  .208014
{txt}{space 9}Age {c |}{col 14}{res}{space 2} .0055265{col 26}{space 2} .0054846{col 37}{space 1}    1.01{col 46}{space 3}0.314{col 54}{space 4}-.0052232{col 67}{space 3} .0162761
{txt}{space 9}Edu {c |}{col 14}{res}{space 2}-.1138078{col 26}{space 2} .1382563{col 37}{space 1}   -0.82{col 46}{space 3}0.410{col 54}{space 4}-.3847851{col 67}{space 3} .1571696
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}  -.36504{col 26}{space 2} .1093366{col 37}{space 1}   -3.34{col 46}{space 3}0.001{col 54}{space 4}-.5793359{col 67}{space 3}-.1507442
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.4573204{col 26}{space 2}  .135805{col 37}{space 1}   -3.37{col 46}{space 3}0.001{col 54}{space 4}-.7234933{col 67}{space 3}-.1911476
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2} -.057673{col 26}{space 2}   .12031{col 37}{space 1}   -0.48{col 46}{space 3}0.632{col 54}{space 4}-.2934763{col 67}{space 3} .1781304
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2}-.0752868{col 26}{space 2} .1228124{col 37}{space 1}   -0.61{col 46}{space 3}0.540{col 54}{space 4}-.3159946{col 67}{space 3}  .165421
{txt}{space 8}Mili {c |}{col 14}{res}{space 2} .3901485{col 26}{space 2} .2654656{col 37}{space 1}    1.47{col 46}{space 3}0.142{col 54}{space 4}-.1301545{col 67}{space 3} .9104515
{txt}{space 6}Income {c |}{col 14}{res}{space 2}-.0168322{col 26}{space 2} .0307204{col 37}{space 1}   -0.55{col 46}{space 3}0.584{col 54}{space 4}-.0770431{col 67}{space 3} .0433787
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.136378{col 26}{space 2} .8404029{col 37}{space 1}    4.92{col 46}{space 3}0.000{col 54}{space 4} 2.489218{col 67}{space 3} 5.783537
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. eststo: logit supportB econcost humancost combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Mili Income Denuke UStroops_withdrawal Intl_reputation War Provo  Nuclear Military  Alliance Japan Endurance, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-655.80427}  
Iteration 1:{space 3}log pseudolikelihood = {res:-479.34026}  
Iteration 2:{space 3}log pseudolikelihood = {res:-470.85938}  
Iteration 3:{space 3}log pseudolikelihood = {res:-470.77473}  
Iteration 4:{space 3}log pseudolikelihood = {res:-470.77469}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:22})}{col 70} = {res}{ralign 6:226.01}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-470.77469}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.2821}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    supportB{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}-1.112419{col 26}{space 2} .2416631{col 37}{space 1}   -4.60{col 46}{space 3}0.000{col 54}{space 4} -1.58607{col 67}{space 3}-.6387683
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-1.309538{col 26}{space 2} .2472045{col 37}{space 1}   -5.30{col 46}{space 3}0.000{col 54}{space 4} -1.79405{col 67}{space 3}-.8250261
{txt}combinedcost {c |}{col 14}{res}{space 2}-1.496848{col 26}{space 2} .2409244{col 37}{space 1}   -6.21{col 46}{space 3}0.000{col 54}{space 4}-1.969051{col 67}{space 3}-1.024645
{txt}{space 6}Gender {c |}{col 14}{res}{space 2} -.175175{col 26}{space 2} .2906255{col 37}{space 1}   -0.60{col 46}{space 3}0.547{col 54}{space 4}-.7447905{col 67}{space 3} .3944404
{txt}{space 9}Age {c |}{col 14}{res}{space 2}-.0068384{col 26}{space 2} .0070148{col 37}{space 1}   -0.97{col 46}{space 3}0.330{col 54}{space 4}-.0205871{col 67}{space 3} .0069103
{txt}{space 9}Edu {c |}{col 14}{res}{space 2}-.2519523{col 26}{space 2} .1548061{col 37}{space 1}   -1.63{col 46}{space 3}0.104{col 54}{space 4}-.5553667{col 67}{space 3}  .051462
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.3555006{col 26}{space 2} .1314969{col 37}{space 1}   -2.70{col 46}{space 3}0.007{col 54}{space 4}-.6132297{col 67}{space 3}-.0977715
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.3935137{col 26}{space 2} .1563011{col 37}{space 1}   -2.52{col 46}{space 3}0.012{col 54}{space 4}-.6998583{col 67}{space 3}-.0871692
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2} .0393831{col 26}{space 2} .1398125{col 37}{space 1}    0.28{col 46}{space 3}0.778{col 54}{space 4}-.2346444{col 67}{space 3} .3134105
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2}-.1077369{col 26}{space 2} .1457785{col 37}{space 1}   -0.74{col 46}{space 3}0.460{col 54}{space 4}-.3934576{col 67}{space 3} .1779837
{txt}{space 8}Mili {c |}{col 14}{res}{space 2} .5509001{col 26}{space 2} .3005167{col 37}{space 1}    1.83{col 46}{space 3}0.067{col 54}{space 4}-.0381018{col 67}{space 3} 1.139902
{txt}{space 6}Income {c |}{col 14}{res}{space 2}-.0475706{col 26}{space 2} .0354399{col 37}{space 1}   -1.34{col 46}{space 3}0.180{col 54}{space 4}-.1170315{col 67}{space 3} .0218902
{txt}{space 6}Denuke {c |}{col 14}{res}{space 2} .1523115{col 26}{space 2}  .105863{col 37}{space 1}    1.44{col 46}{space 3}0.150{col 54}{space 4}-.0551762{col 67}{space 3} .3597993
{txt}UStroops_w~l {c |}{col 14}{res}{space 2}-.2359616{col 26}{space 2} .1118502{col 37}{space 1}   -2.11{col 46}{space 3}0.035{col 54}{space 4}-.4551839{col 67}{space 3}-.0167393
{txt}Intl_reput~n {c |}{col 14}{res}{space 2} .7927538{col 26}{space 2}  .112181{col 37}{space 1}    7.07{col 46}{space 3}0.000{col 54}{space 4} .5728831{col 67}{space 3} 1.012625
{txt}{space 9}War {c |}{col 14}{res}{space 2}-.3989733{col 26}{space 2} .1166796{col 37}{space 1}   -3.42{col 46}{space 3}0.001{col 54}{space 4}-.6276611{col 67}{space 3}-.1702854
{txt}{space 7}Provo {c |}{col 14}{res}{space 2} .2030805{col 26}{space 2} .1103221{col 37}{space 1}    1.84{col 46}{space 3}0.066{col 54}{space 4}-.0131469{col 67}{space 3} .4193079
{txt}{space 5}Nuclear {c |}{col 14}{res}{space 2}-.0711076{col 26}{space 2}  .097231{col 37}{space 1}   -0.73{col 46}{space 3}0.465{col 54}{space 4}-.2616768{col 67}{space 3} .1194617
{txt}{space 4}Military {c |}{col 14}{res}{space 2}-.0992805{col 26}{space 2} .1340796{col 37}{space 1}   -0.74{col 46}{space 3}0.459{col 54}{space 4}-.3620717{col 67}{space 3} .1635107
{txt}{space 4}Alliance {c |}{col 14}{res}{space 2} -.045783{col 26}{space 2} .1301659{col 37}{space 1}   -0.35{col 46}{space 3}0.725{col 54}{space 4}-.3009036{col 67}{space 3} .2093375
{txt}{space 7}Japan {c |}{col 14}{res}{space 2}-.8157795{col 26}{space 2} .0927094{col 37}{space 1}   -8.80{col 46}{space 3}0.000{col 54}{space 4}-.9974866{col 67}{space 3}-.6340725
{txt}{space 3}Endurance {c |}{col 14}{res}{space 2} .0525274{col 26}{space 2} .0698563{col 37}{space 1}    0.75{col 46}{space 3}0.452{col 54}{space 4}-.0843884{col 67}{space 3} .1894432
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.604112{col 26}{space 2} 1.152812{col 37}{space 1}    4.86{col 46}{space 3}0.000{col 54}{space 4} 3.344642{col 67}{space 3} 7.863583
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. 
. esttab using table1.tex, replace booktabs alignment(l) ///
> b(a3) se scalars("N Obs." "ll Log Likelihood" ) ///
> title(My Table)  star(+ 0.10 * 0.05 ** 0.01 *** 0.001) nogaps //
{res}{txt}(output written to {browse  `"table1.tex"'})

{com}. 
. ********************************************************************************
. ** Table 10: Robustness Check IV: Ordered Logistic Regression Analysis
. ********************************************************************************
. 
. eststo clear
{txt}
{com}. eststo: ologit support econcost humancost combinedcost, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1929.0775}  
Iteration 1:{space 3}log pseudolikelihood = {res: -1880.712}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1880.3885}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1880.3883}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:3})}{col 70} = {res}{ralign 6:85.85}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1880.3883}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0252}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     support{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}-1.245761{col 26}{space 2} .1602048{col 37}{space 1}   -7.78{col 46}{space 3}0.000{col 54}{space 4}-1.559757{col 67}{space 3}-.9317656
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-1.024471{col 26}{space 2} .1658942{col 37}{space 1}   -6.18{col 46}{space 3}0.000{col 54}{space 4}-1.349617{col 67}{space 3}-.6993239
{txt}combinedcost {c |}{col 14}{res}{space 2}-1.416836{col 26}{space 2} .1635524{col 37}{space 1}   -8.66{col 46}{space 3}0.000{col 54}{space 4}-1.737392{col 67}{space 3}-1.096279
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-3.950127{col 26}{space 2}  .194096{col 54}{space 4}-4.330548{col 67}{space 3}-3.569706
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} -2.75624{col 26}{space 2} .1528719{col 54}{space 4}-3.055864{col 67}{space 3}-2.456617
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-1.709309{col 26}{space 2} .1338186{col 54}{space 4}-1.971588{col 67}{space 3}-1.447029
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} -1.12633{col 26}{space 2}  .128184{col 54}{space 4}-1.377566{col 67}{space 3}-.8750935
{txt}{space 7}/cut5 {c |}{col 14}{res}{space 2} .0121732{col 26}{space 2} .1198339{col 54}{space 4} -.222697{col 67}{space 3} .2470433
{txt}{space 7}/cut6 {c |}{col 14}{res}{space 2}  1.25196{col 26}{space 2} .1300451{col 54}{space 4} .9970759{col 67}{space 3} 1.506843
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: ologit support econcost humancost combinedcost Gender Age Edu Ideo PID Inter_intl Know_intl Mili Income Denuke UStroops_withdrawal Intl_reputation War Provo Nuclear Military Alliance Japan Endurance, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1929.0775}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1625.4338}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1601.8558}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1601.6254}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1601.6254}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,040}
{txt}{col 57}{lalign 13:Wald chi2({res:22})}{col 70} = {res}{ralign 6:529.44}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1601.6254}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1697}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     support{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}econcost {c |}{col 14}{res}{space 2}-1.359124{col 26}{space 2} .1676265{col 37}{space 1}   -8.11{col 46}{space 3}0.000{col 54}{space 4}-1.687666{col 67}{space 3}-1.030582
{txt}{space 3}humancost {c |}{col 14}{res}{space 2}-1.364172{col 26}{space 2} .1632568{col 37}{space 1}   -8.36{col 46}{space 3}0.000{col 54}{space 4} -1.68415{col 67}{space 3}-1.044195
{txt}combinedcost {c |}{col 14}{res}{space 2}-1.588682{col 26}{space 2} .1687611{col 37}{space 1}   -9.41{col 46}{space 3}0.000{col 54}{space 4}-1.919448{col 67}{space 3}-1.257916
{txt}{space 6}Gender {c |}{col 14}{res}{space 2}-.3620825{col 26}{space 2} .2115083{col 37}{space 1}   -1.71{col 46}{space 3}0.087{col 54}{space 4}-.7766312{col 67}{space 3} .0524662
{txt}{space 9}Age {c |}{col 14}{res}{space 2}-.0038114{col 26}{space 2} .0047695{col 37}{space 1}   -0.80{col 46}{space 3}0.424{col 54}{space 4}-.0131596{col 67}{space 3} .0055367
{txt}{space 9}Edu {c |}{col 14}{res}{space 2}-.1026104{col 26}{space 2} .1084989{col 37}{space 1}   -0.95{col 46}{space 3}0.344{col 54}{space 4}-.3152643{col 67}{space 3} .1100436
{txt}{space 8}Ideo {c |}{col 14}{res}{space 2}-.2891352{col 26}{space 2} .0946596{col 37}{space 1}   -3.05{col 46}{space 3}0.002{col 54}{space 4}-.4746646{col 67}{space 3}-.1036058
{txt}{space 9}PID {c |}{col 14}{res}{space 2}-.1867645{col 26}{space 2} .1066805{col 37}{space 1}   -1.75{col 46}{space 3}0.080{col 54}{space 4}-.3958543{col 67}{space 3} .0223254
{txt}{space 2}Inter_intl {c |}{col 14}{res}{space 2}-.0187756{col 26}{space 2}  .105203{col 37}{space 1}   -0.18{col 46}{space 3}0.858{col 54}{space 4}-.2249696{col 67}{space 3} .1874184
{txt}{space 3}Know_intl {c |}{col 14}{res}{space 2}-.0144203{col 26}{space 2} .1151118{col 37}{space 1}   -0.13{col 46}{space 3}0.900{col 54}{space 4}-.2400353{col 67}{space 3} .2111947
{txt}{space 8}Mili {c |}{col 14}{res}{space 2}  .237344{col 26}{space 2} .2181421{col 37}{space 1}    1.09{col 46}{space 3}0.277{col 54}{space 4}-.1902067{col 67}{space 3} .6648947
{txt}{space 6}Income {c |}{col 14}{res}{space 2}-.0118376{col 26}{space 2} .0257061{col 37}{space 1}   -0.46{col 46}{space 3}0.645{col 54}{space 4}-.0622207{col 67}{space 3} .0385455
{txt}{space 6}Denuke {c |}{col 14}{res}{space 2} .2115968{col 26}{space 2}  .082074{col 37}{space 1}    2.58{col 46}{space 3}0.010{col 54}{space 4} .0507348{col 67}{space 3} .3724588
{txt}UStroops_w~l {c |}{col 14}{res}{space 2}-.1080161{col 26}{space 2} .0776194{col 37}{space 1}   -1.39{col 46}{space 3}0.164{col 54}{space 4}-.2601473{col 67}{space 3} .0441152
{txt}Intl_reput~n {c |}{col 14}{res}{space 2}  .684782{col 26}{space 2} .0811823{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .5256676{col 67}{space 3} .8438964
{txt}{space 9}War {c |}{col 14}{res}{space 2}-.2916166{col 26}{space 2} .0810948{col 37}{space 1}   -3.60{col 46}{space 3}0.000{col 54}{space 4}-.4505595{col 67}{space 3}-.1326737
{txt}{space 7}Provo {c |}{col 14}{res}{space 2} .1261672{col 26}{space 2} .0830878{col 37}{space 1}    1.52{col 46}{space 3}0.129{col 54}{space 4}-.0366819{col 67}{space 3} .2890163
{txt}{space 5}Nuclear {c |}{col 14}{res}{space 2}-.0440758{col 26}{space 2} .0700519{col 37}{space 1}   -0.63{col 46}{space 3}0.529{col 54}{space 4} -.181375{col 67}{space 3} .0932234
{txt}{space 4}Military {c |}{col 14}{res}{space 2}-.3527688{col 26}{space 2} .0946884{col 37}{space 1}   -3.73{col 46}{space 3}0.000{col 54}{space 4}-.5383546{col 67}{space 3} -.167183
{txt}{space 4}Alliance {c |}{col 14}{res}{space 2}-.1286782{col 26}{space 2} .0964234{col 37}{space 1}   -1.33{col 46}{space 3}0.182{col 54}{space 4}-.3176646{col 67}{space 3} .0603082
{txt}{space 7}Japan {c |}{col 14}{res}{space 2}-.7198912{col 26}{space 2} .0645744{col 37}{space 1}  -11.15{col 46}{space 3}0.000{col 54}{space 4}-.8464547{col 67}{space 3}-.5933278
{txt}{space 3}Endurance {c |}{col 14}{res}{space 2} .2350311{col 26}{space 2} .0494491{col 37}{space 1}    4.75{col 46}{space 3}0.000{col 54}{space 4} .1381126{col 67}{space 3} .3319495
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-6.854008{col 26}{space 2}  .866192{col 54}{space 4}-8.551713{col 67}{space 3}-5.156302
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-5.378515{col 26}{space 2} .8457657{col 54}{space 4}-7.036185{col 67}{space 3}-3.720845
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-3.954595{col 26}{space 2} .8421606{col 54}{space 4}-5.605199{col 67}{space 3} -2.30399
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2}-3.109238{col 26}{space 2} .8404342{col 54}{space 4}-4.756458{col 67}{space 3}-1.462017
{txt}{space 7}/cut5 {c |}{col 14}{res}{space 2}-1.482845{col 26}{space 2} .8377382{col 54}{space 4}-3.124782{col 67}{space 3} .1590912
{txt}{space 7}/cut6 {c |}{col 14}{res}{space 2} .1558262{col 26}{space 2} .8410027{col 54}{space 4}-1.492509{col 67}{space 3} 1.804161
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. esttab using table1.tex, replace booktabs alignment(l) ///
> b(a3) se scalars("N Obs." "ll Log Likelihood" ) ///
> title(My Table) star(+ 0.10 * 0.05 ** 0.01 *** 0.001) nogaps //
{res}{txt}(output written to {browse  `"table1.tex"'})

{com}. 
. 
. ********************************************************************************
. ** Table 11: Randomization Checks: Kolmogorov-Smirnov test for Covariate Balances
. ********************************************************************************
. 
. scalar drop _all
{txt}
{com}. 
. ksmirnov Gender if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.1026       0.061
{txt}2) 그룹2           {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.1026       0.122

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 533 observations.

{com}. scalar a1 = round(r(D), .0001)
{txt}
{com}. scalar a1p = round(r(p), .001)
{txt}
{com}. ksmirnov Gender if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0845       0.149
{txt}3) 그룹3           {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0845       0.298

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 534 observations.

{com}. scalar b1 = round(r(D), .0001)
{txt}
{com}. scalar b1p = round(r(p), .001)
{txt}
{com}. ksmirnov Gender if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0979       0.077
{txt}4) 그룹4           {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0979       0.153

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. scalar c1 = round(r(D), .0001)
{txt}
{com}. scalar c1p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Age if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0221       0.878
{txt}2) 그룹2           {res} -0.1752       0.000
{txt}Combined K-S       {res}  0.1752       0.001

{txt}Note: Ties exist in combined dataset;
      there are 50 unique values out of 533 observations.

{com}. scalar a2 = round(r(D), .0001)
{txt}
{com}. scalar a2p = round(r(p), .001)
{txt}
{com}. ksmirnov Age if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0100       0.973
{txt}3) 그룹3           {res} -0.1702       0.000
{txt}Combined K-S       {res}  0.1702       0.001

{txt}Note: Ties exist in combined dataset;
      there are 50 unique values out of 534 observations.

{com}. scalar b2 = round(r(D), .0001)
{txt}
{com}. scalar b2p = round(r(p), .001)
{txt}
{com}. ksmirnov Age if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0458       0.570
{txt}4) 그룹4           {res} -0.0722       0.248
{txt}Combined K-S       {res}  0.0722       0.488

{txt}Note: Ties exist in combined dataset;
      there are 50 unique values out of 537 observations.

{com}. scalar c2 = round(r(D), .0001)
{txt}
{com}. scalar c2p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Edu if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0122       0.961
{txt}2) 그룹2           {res} -0.0161       0.934
{txt}Combined K-S       {res}  0.0161       1.000

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 533 observations.

{com}. scalar a3 = round(r(D), .0001)
{txt}
{com}. scalar a3p = round(r(p), .001)
{txt}
{com}. ksmirnov Edu if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0367       0.698
{txt}3) 그룹3           {res} -0.0040       0.996
{txt}Combined K-S       {res}  0.0367       0.994

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 534 observations.

{com}. scalar b3 = round(r(D), .0001)
{txt}
{com}. scalar b3p = round(r(p), .001)
{txt}
{com}. ksmirnov Edu if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0062       0.990
{txt}4) 그룹4           {res} -0.0007       1.000
{txt}Combined K-S       {res}  0.0062       1.000

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 537 observations.

{com}. scalar c3 = round(r(D), .0001)
{txt}
{com}. scalar c3p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Ideo if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0313       0.771
{txt}2) 그룹2           {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0313       1.000

{txt}Note: Ties exist in combined dataset;
      there are 3 unique values out of 533 observations.

{com}. scalar a4 = round(r(D), .0001)
{txt}
{com}. scalar a4p = round(r(p), .001)
{txt}
{com}. ksmirnov Ideo if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0000       1.000
{txt}3) 그룹3           {res} -0.0607       0.375
{txt}Combined K-S       {res}  0.0607       0.711

{txt}Note: Ties exist in combined dataset;
      there are 3 unique values out of 534 observations.

{com}. scalar b4 = round(r(D), .0001)
{txt}
{com}. scalar b4p = round(r(p), .001)
{txt}
{com}. ksmirnov Ideo if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0000       1.000
{txt}4) 그룹4           {res} -0.0597       0.385
{txt}Combined K-S       {res}  0.0597       0.726

{txt}Note: Ties exist in combined dataset;
      there are 3 unique values out of 537 observations.

{com}. scalar c4 = round(r(D), .0001)
{txt}
{com}. scalar c4p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov PID if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0387       0.672
{txt}2) 그룹2           {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0387       0.989

{txt}Note: Ties exist in combined dataset;
      there are 3 unique values out of 533 observations.

{com}. scalar a5 = round(r(D), .0001)
{txt}
{com}. scalar a5p = round(r(p), .001)
{txt}
{com}. ksmirnov PID if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0232       0.866
{txt}3) 그룹3           {res} -0.0461       0.568
{txt}Combined K-S       {res}  0.0461       0.940

{txt}Note: Ties exist in combined dataset;
      there are 3 unique values out of 534 observations.

{com}. scalar b5 = round(r(D), .0001)
{txt}
{com}. scalar b5p = round(r(p), .001)
{txt}
{com}. ksmirnov PID if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0089       0.979
{txt}4) 그룹4           {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0089       1.000

{txt}Note: Ties exist in combined dataset;
      there are 3 unique values out of 537 observations.

{com}. scalar c5 = round(r(D), .0001)
{txt}
{com}. scalar c5p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Inter_intl if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0106       0.971
{txt}2) 그룹2           {res} -0.0253       0.844
{txt}Combined K-S       {res}  0.0253       1.000

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 533 observations.

{com}. scalar a6 = round(r(D), .0001)
{txt}
{com}. scalar a6p = round(r(p), .001)
{txt}
{com}. ksmirnov Inter_intl if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0144       0.946
{txt}3) 그룹3           {res} -0.0319       0.763
{txt}Combined K-S       {res}  0.0319       0.999

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 534 observations.

{com}. scalar b6 = round(r(D), .0001)
{txt}
{com}. scalar b6p = round(r(p), .001)
{txt}
{com}. ksmirnov Inter_intl if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0085       0.981
{txt}4) 그룹4           {res} -0.0437       0.600
{txt}Combined K-S       {res}  0.0437       0.960

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 537 observations.

{com}. scalar c6 = round(r(D), .0001)
{txt}
{com}. scalar c6p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Know_intl if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0253       0.844
{txt}2) 그룹2           {res} -0.0450       0.584
{txt}Combined K-S       {res}  0.0450       0.951

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 533 observations.

{com}. scalar a7 = round(r(D), .0001)
{txt}
{com}. scalar a7p = round(r(p), .001)
{txt}
{com}. ksmirnov Know_intl if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0000       1.000
{txt}3) 그룹3           {res} -0.0395       0.660
{txt}Combined K-S       {res}  0.0395       0.985

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 534 observations.

{com}. scalar b7 = round(r(D), .0001)
{txt}
{com}. scalar b7p = round(r(p), .001)
{txt}
{com}. ksmirnov Know_intl if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0084       0.981
{txt}4) 그룹4           {res} -0.0656       0.316
{txt}Combined K-S       {res}  0.0656       0.611

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 537 observations.

{com}. scalar c7 = round(r(D), .0001)
{txt}
{com}. scalar c7p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Mili if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0000       1.000
{txt}2) 그룹2           {res} -0.1317       0.010
{txt}Combined K-S       {res}  0.1317       0.020

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 533 observations.

{com}. scalar a8 = round(r(D), .0001)
{txt}
{com}. scalar a8p = round(r(p), .001)
{txt}
{com}. ksmirnov Mili if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0000       1.000
{txt}3) 그룹3           {res} -0.1132       0.033
{txt}Combined K-S       {res}  0.1132       0.066

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 534 observations.

{com}. scalar b8= round(r(D), .0001)
{txt}
{com}. scalar b8p = round(r(p), .001)
{txt}
{com}. ksmirnov Mili if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0000       1.000
{txt}4) 그룹4           {res} -0.1176       0.025
{txt}Combined K-S       {res}  0.1176       0.049

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. scalar c8 = round(r(D), .0001)
{txt}
{com}. scalar c8p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Income if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0293       0.796
{txt}2) 그룹2           {res} -0.0220       0.880
{txt}Combined K-S       {res}  0.0293       1.000

{txt}Note: Ties exist in combined dataset;
      there are 11 unique values out of 533 observations.

{com}. scalar a9 = round(r(D), .0001)
{txt}
{com}. scalar a9p = round(r(p), .001)
{txt}
{com}. ksmirnov Income if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0109       0.969
{txt}3) 그룹3           {res} -0.0605       0.377
{txt}Combined K-S       {res}  0.0605       0.714

{txt}Note: Ties exist in combined dataset;
      there are 11 unique values out of 534 observations.

{com}. scalar b9 = round(r(D), .0001)
{txt}
{com}. scalar b9p = round(r(p), .001)
{txt}
{com}. ksmirnov Income if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0242       0.855
{txt}4) 그룹4           {res} -0.0135       0.953
{txt}Combined K-S       {res}  0.0242       1.000

{txt}Note: Ties exist in combined dataset;
      there are 11 unique values out of 537 observations.

{com}. scalar c9 = round(r(D), .0001)
{txt}
{com}. scalar c9p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Denuke if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0071       0.987
{txt}2) 그룹2           {res} -0.1249       0.016
{txt}Combined K-S       {res}  0.1249       0.032

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 533 observations.

{com}. scalar a10 = round(r(D), .0001)
{txt}
{com}. scalar a10p = round(r(p), .001)
{txt}
{com}. ksmirnov Denuke if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0000       1.000
{txt}3) 그룹3           {res} -0.0584       0.403
{txt}Combined K-S       {res}  0.0584       0.754

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 534 observations.

{com}. scalar b10 = round(r(D), .0001)
{txt}
{com}. scalar b10p = round(r(p), .001)
{txt}
{com}. ksmirnov Denuke if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0000       1.000
{txt}4) 그룹4           {res} -0.0896       0.116
{txt}Combined K-S       {res}  0.0896       0.232

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 537 observations.

{com}. scalar c10 = round(r(D), .0001)
{txt}
{com}. scalar c10p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov UStroops_withdrawal if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0045       0.995
{txt}2) 그룹2           {res} -0.0426       0.618
{txt}Combined K-S       {res}  0.0426       0.970

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 533 observations.

{com}. scalar a11 = round(r(D), .0001)
{txt}
{com}. scalar a11p = round(r(p), .001)
{txt}
{com}. ksmirnov UStroops_withdrawal if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0084       0.982
{txt}3) 그룹3           {res} -0.0172       0.924
{txt}Combined K-S       {res}  0.0172       1.000

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 534 observations.

{com}. scalar b11 = round(r(D), .0001)
{txt}
{com}. scalar b11p = round(r(p), .001)
{txt}
{com}. ksmirnov UStroops_withdrawal if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0000       1.000
{txt}4) 그룹4           {res} -0.0638       0.336
{txt}Combined K-S       {res}  0.0638       0.646

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 537 observations.

{com}. scalar c11 = round(r(D), .0001)
{txt}
{com}. scalar c11p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Intl_reputation if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0271       0.823
{txt}2) 그룹2           {res} -0.1046       0.055
{txt}Combined K-S       {res}  0.1046       0.110

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 533 observations.

{com}. scalar a12 = round(r(D), .0001)
{txt}
{com}. scalar a12p = round(r(p), .001)
{txt}
{com}. ksmirnov Intl_reputation if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0231       0.867
{txt}3) 그룹3           {res} -0.0127       0.958
{txt}Combined K-S       {res}  0.0231       1.000

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 534 observations.

{com}. scalar b12 = round(r(D), .0001)
{txt}
{com}. scalar b12p = round(r(p), .001)
{txt}
{com}. ksmirnov Intl_reputation if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0076       0.985
{txt}4) 그룹4           {res} -0.1144       0.030
{txt}Combined K-S       {res}  0.1144       0.060

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 537 observations.

{com}. scalar c12 = round(r(D), .0001)
{txt}
{com}. scalar c12p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov War if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0169       0.927
{txt}2) 그룹2           {res} -0.0631       0.347
{txt}Combined K-S       {res}  0.0631       0.666

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 533 observations.

{com}. scalar a13 =round(r(D), .0001)
{txt}
{com}. scalar a13p = round(r(p), .001)
{txt}
{com}. ksmirnov War if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0010       1.000
{txt}3) 그룹3           {res} -0.0570       0.421
{txt}Combined K-S       {res}  0.0570       0.780

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 534 observations.

{com}. scalar b13 =round(r(D), .0001)
{txt}
{com}. scalar b13p = round(r(p), .001)
{txt}
{com}. ksmirnov War if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0000       1.000
{txt}4) 그룹4           {res} -0.0860       0.138
{txt}Combined K-S       {res}  0.0860       0.275

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 537 observations.

{com}. scalar c13 =round(r(D), .0001)
{txt}
{com}. scalar c13p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Provo if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0329       0.750
{txt}2) 그룹2           {res} -0.0256       0.840
{txt}Combined K-S       {res}  0.0329       0.999

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 533 observations.

{com}. scalar a14 = round(r(D), .0001)
{txt}
{com}. scalar a14p = round(r(p), .001)
{txt}
{com}. ksmirnov Provo if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0000       1.000
{txt}3) 그룹3           {res} -0.0704       0.267
{txt}Combined K-S       {res}  0.0704       0.524

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 534 observations.

{com}. scalar b14 = round(r(D), .0001)
{txt}
{com}. scalar b14p = round(r(p), .001)
{txt}
{com}. ksmirnov Provo if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0000       1.000
{txt}4) 그룹4           {res} -0.0536       0.463
{txt}Combined K-S       {res}  0.0536       0.836

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 537 observations.

{com}. scalar c14 = round(r(D), .0001)
{txt}
{com}. scalar c14p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Nuclear if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0364       0.704
{txt}2) 그룹2           {res} -0.0149       0.942
{txt}Combined K-S       {res}  0.0364       0.995

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 533 observations.

{com}. scalar a15 = round(r(D), .0001)
{txt}
{com}. scalar a15p = round(r(p), .001)
{txt}
{com}. ksmirnov Nuclear  if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0390       0.667
{txt}3) 그룹3           {res} -0.0767       0.209
{txt}Combined K-S       {res}  0.0767       0.413

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 534 observations.

{com}. scalar b15 = round(r(D), .0001)
{txt}
{com}. scalar b15p = round(r(p), .001)
{txt}
{com}. ksmirnov Nuclear  if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0351       0.719
{txt}4) 그룹4           {res} -0.0318       0.763
{txt}Combined K-S       {res}  0.0351       0.997

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 537 observations.

{com}. scalar c15 = round(r(D), .0001)
{txt}
{com}. scalar c15p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Military if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0846       0.150
{txt}2) 그룹2           {res} -0.0073       0.986
{txt}Combined K-S       {res}  0.0846       0.298

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 533 observations.

{com}. scalar a16 = round(r(D), .0001)
{txt}
{com}. scalar a16p = round(r(p), .001)
{txt}
{com}. ksmirnov Military if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0082       0.982
{txt}3) 그룹3           {res} -0.0017       0.999
{txt}Combined K-S       {res}  0.0082       1.000

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 534 observations.

{com}. scalar b16 = round(r(D), .0001)
{txt}
{com}. scalar b16p = round(r(p), .001)
{txt}
{com}. ksmirnov Military if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0926       0.101
{txt}4) 그룹4           {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0926       0.201

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 537 observations.

{com}. scalar c16 = round(r(D), .0001)
{txt}
{com}. scalar c16p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Alliance if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0696       0.276
{txt}2) 그룹2           {res} -0.0177       0.920
{txt}Combined K-S       {res}  0.0696       0.540

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 533 observations.

{com}. scalar a17 = round(r(D), .0001)
{txt}
{com}. scalar a17p = round(r(p), .001)
{txt}
{com}. ksmirnov Alliance if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0368       0.697
{txt}3) 그룹3           {res} -0.0401       0.652
{txt}Combined K-S       {res}  0.0401       0.983

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 534 observations.

{com}. scalar b17 = round(r(D), .0001)
{txt}
{com}. scalar b17p = round(r(p), .001)
{txt}
{com}. ksmirnov Alliance if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0476       0.544
{txt}4) 그룹4           {res} -0.0023       0.999
{txt}Combined K-S       {res}  0.0476       0.921

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 537 observations.

{com}. scalar c17 = round(r(D), .0001)
{txt}
{com}. scalar c17p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Japan if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0741       0.232
{txt}2) 그룹2           {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0741       0.459

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 533 observations.

{com}. scalar a18 = round(r(D), .0001)
{txt}
{com}. scalar a18p = round(r(p), .001)
{txt}
{com}. ksmirnov Japan if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.0168       0.928
{txt}3) 그룹3           {res} -0.0225       0.873
{txt}Combined K-S       {res}  0.0225       1.000

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 534 observations.

{com}. scalar b18 = round(r(D), .0001)
{txt}
{com}. scalar b18p = round(r(p), .001)
{txt}
{com}. ksmirnov Japan if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.1129       0.033
{txt}4) 그룹4           {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.1129       0.066

{txt}Note: Ties exist in combined dataset;
      there are 5 unique values out of 537 observations.

{com}. scalar c18 = round(r(D), .0001)
{txt}
{com}. scalar c18p = round(r(p), .001)
{txt}
{com}. 
. ksmirnov Endurance if Group ==2 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.2162       0.000
{txt}2) 그룹2           {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.2162       0.000

{txt}Note: Ties exist in combined dataset;
      there are 6 unique values out of 533 observations.

{com}. scalar a19 = round(r(D), .0001)
{txt}
{com}. scalar a19p = round(r(p), .001)
{txt}
{com}. ksmirnov Endurance if Group ==3 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.1353       0.008
{txt}3) 그룹3           {res} -0.0085       0.981
{txt}Combined K-S       {res}  0.1353       0.015

{txt}Note: Ties exist in combined dataset;
      there are 6 unique values out of 534 observations.

{com}. scalar b19 = round(r(D), .0001)
{txt}
{com}. scalar b19p = round(r(p), .001)
{txt}
{com}. ksmirnov Endurance if Group ==4 | Group ==1, by(Group) 

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
1) 그룹1           {res}  0.1749       0.000
{txt}4) 그룹4           {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.1749       0.001

{txt}Note: Ties exist in combined dataset;
      there are 6 unique values out of 537 observations.

{com}. scalar c19 = round(r(D), .0001)
{txt}
{com}. scalar c19p = round(r(p), .001)
{txt}
{com}. 
. matrix A = (a1, b1, c1 \ a1p, b1p, c1p \ a2, b2, c2 \ a2p, b2p, c2p \ a3, b3, c3 \ a3p, b3p, c3p \ a4, b4, c4 \ a4p, b4p, c4p \ a5, b5, c5 \ a5p, b5p, c5p \a6, b6, c6  \ a6p, b6p, c6p \ a7, b7, c7 \ a7p, b7p, c7p \a8, b8, c8 \ a8p, b8p, c8p \a9, b9, c9  \ a9p, b9p, c9p \a10, b10, c10 \ a10p, b10p, c10p \a11, b11, c11 \ a11p, b11p, c11p \a12, b12, c12 \ a12p, b12p, c12p \a13, b13, c13 \ a13p, b13p, c13p \a14, b14, c14 \ a14p, b14p, c14p \a15, b15, c15 \ a15p, b15p, c15p \a16, b16, c16 \ a16p, b16p, c16p \a17, b17, c17 \ a17p, b17p, c17p \a18, b18, c18 \ a18p, b18p, c18p \a19, b19, c19 \ a19p, b19p, c19p ) 
{txt}
{com}. 
. matrix list A
{res}
{txt}A[38,3]
        c1     c2     c3
 r1 {res} .1026  .0845  .0979
{txt} r2 {res}  .122   .298   .153
{txt} r3 {res} .1752  .1702  .0722
{txt} r4 {res}  .001   .001   .488
{txt} r5 {res} .0161  .0367  .0062
{txt} r6 {res}     1   .994      1
{txt} r7 {res} .0313  .0607  .0597
{txt} r8 {res}     1   .711   .726
{txt} r9 {res} .0387  .0461  .0089
{txt}r10 {res}  .989    .94      1
{txt}r11 {res} .0253  .0319  .0437
{txt}r12 {res}     1   .999    .96
{txt}r13 {res}  .045  .0395  .0656
{txt}r14 {res}  .951   .985   .611
{txt}r15 {res} .1317  .1132  .1176
{txt}r16 {res}   .02   .066   .049
{txt}r17 {res} .0293  .0605  .0242
{txt}r18 {res}     1   .714      1
{txt}r19 {res} .1249  .0584  .0896
{txt}r20 {res}  .032   .754   .232
{txt}r21 {res} .0426  .0172  .0638
{txt}r22 {res}   .97      1   .646
{txt}r23 {res} .1046  .0231  .1144
{txt}r24 {res}   .11      1    .06
{txt}r25 {res} .0631   .057   .086
{txt}r26 {res}  .666    .78   .275
{txt}r27 {res} .0329  .0704  .0536
{txt}r28 {res}  .999   .524   .836
{txt}r29 {res} .0364  .0767  .0351
{txt}r30 {res}  .995   .413   .997
{txt}r31 {res} .0846  .0082  .0926
{txt}r32 {res}  .298      1   .201
{txt}r33 {res} .0696  .0401  .0476
{txt}r34 {res}   .54   .983   .921
{txt}r35 {res} .0741  .0225  .1129
{txt}r36 {res}  .459      1   .066
{txt}r37 {res} .2162  .1353  .1749
{txt}r38 {res}     0   .015   .001
{reset}
{com}. 
. esttab matrix(A) using table1.tex, replace star(+ 0.10 * 0.05 ** .01 *** .001) nogaps
{res}{txt}(output written to {browse  `"table1.tex"'})

{com}. 
. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\user\Dropbox\연구 프로젝트\South Korean Cost Sensitivity and Support for Nuclear Weapons\II Submission\Data Replication\replication.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 4 Apr 2024, 02:19:28
{txt}{.-}
{smcl}
{txt}{sf}{ul off}